Classification (13.01.2011)

Video in TIB AV-Portal: Classification (13.01.2011)

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Classification (13.01.2011)
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10.5446/327 (DOI)
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Technische Universität Braunschweig
Institut für Informationssysteme
Balke, Wolf-Tilo
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In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures. Course will be tought completly in English. The general structure of the course is: Typical dw use case scenarios Basic architecture of dw Data modelling on a conceptual, logical and physical level Multidimensional E/R modelling Cubes, dimensions, measures Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot MOLAP, ROLAP, HOLAP SQL99 OLAP operators, MDX Snowflake, star and starflake schemas for relational storage Multimedia physical storage (linearization) DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes Other optimization procedures: data partitioning, star join optimization, materialized views ETL Association rule mining, sequence patterns, time series Classification: Decision trees, naive Bayes classifications, SVM Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis
Linear regression State of matter Multiplication sign Electronic mailing list Time series Planning Mathematical analysis Average Mereology Sequence Ähnlichkeitssuche Sequence Data mining Sign (mathematics) Data mining Videoconferencing Right angle Musical ensemble Category of being Series (mathematics)
Point (geometry) State of matter Decision theory Characteristic polynomial Virtual machine 1 (number) Set (mathematics) Insertion loss Disk read-and-write head Law of large numbers Attribute grammar Product (business) Data model Set (mathematics) Row (database) Physical law Software testing Testmenge Endliche Modelltheorie Social class Metropolitan area network Decision theory Attribute grammar Funktionalanalysis Euler angles System call Type theory Array data structure Personal digital assistant Function (mathematics) Uniform resource name Network topology Website Social class Software testing Heuristic Object (grammar) Row (database) Wide area network
Asynchronous Transfer Mode Group action Algorithm Plastikkarte Emulation Product (business) Data model Physical law Software testing Process (computing) Inductive reasoning Social class Pattern recognition Algorithm Matching (graph theory) Plastikkarte Field (computer science) Database transaction Statistics Category of being Process (computing) Grand Unified Theory Software testing Whiteboard Optical disc drive
Algorithm Decision theory 3 (number) Set (mathematics) Mereology Smith chart Neuroinformatik Number Data model Different (Kate Ryan album) Software testing Testmenge Endliche Modelltheorie Office suite Inductive reasoning Social class Rule of inference Modal logic Algorithm Decision theory Mathematical analysis Field (computer science) Instance (computer science) TLA <Logik> Network topology System identification Row (database)
Rule of inference Algorithm Decision theory Decision theory Field (computer science) Disk read-and-write head Rule of inference Smith chart Data model Goodness of fit Roundness (object) Testdaten Personal digital assistant Semiconductor memory Uniform resource name Ring (mathematics) Computer science Software testing Table (information) Routing Inductive reasoning Social class Row (database)
Cluster sampling Group action State of matter Decision theory Execution unit Virtual machine Set (mathematics) Decision tree learning Rule of inference Attribute grammar Measurement Goodness of fit Unsupervised learning Natural number Set (mathematics) Circle Software testing Damping Data structure Social class Rule of inference Decision tree learning Computer network Cartesian coordinate system Existence Uniform boundedness principle Array data structure Software Personal digital assistant Network topology Social class Whiteboard Bayesian network
Klassifikations- und Regressionsbaum Existence Decision tree learning Distribution (mathematics) Algorithm Decision theory Maxima and minima Branch (computer science) Decision tree learning Goodness of fit Root Network topology Inductive reasoning Algorithm Decision theory Linear regression Graph (mathematics) Moment (mathematics) Computer Branch (computer science) Attribute grammar Student's t-test Flow separation System call Similarity (geometry) Data mining Network topology Software testing Social class Right angle Whiteboard Data structure
Point (geometry) Group action Algorithm Multiplication sign Division (mathematics) 3 (number) Set (mathematics) Online help Insertion loss Branch (computer science) Decision tree learning Mereology Emulation Heegaard splitting Pointer (computer programming) Different (Kate Ryan album) Number theory Single-precision floating-point format Set (mathematics) Electronic visual display Data structure Inductive reasoning Social class Physical system Default (computer science) MIDI Information management Algorithm Information Sine Moment (mathematics) Interior (topology) Attribute grammar Subset Single-precision floating-point format Exterior algebra Personal digital assistant Uniform resource name Order (biology) Social class Software testing Heuristic Object (grammar) Game theory Data structure Row (database) Wide area network
Point (geometry) Axiom of choice Purchasing Group action Algorithm Multiplication sign 1 (number) Insertion loss Nominal number Attribute grammar Heegaard splitting Type theory Strategy game Bit rate Different (Kate Ryan album) Authorization Inverter (logic gate) Software testing Integer Condition number Level of measurement Polygon mesh Military base Attribute grammar Nominal number Continuous function Binary file Category of being Type theory Number Exterior algebra Personal digital assistant Heegaard splitting Strategy game Software testing Condition number Right angle Row (database)
Computer virus Ewe language Group action Dynamical system INTEGRAL Multiplication sign Decision theory Equaliser (mathematics) Real number Morley's categoricity theorem Nominal number Arm Computer icon Heegaard splitting Causality Dedekind cut Different (Kate Ryan album) Term (mathematics) Single-precision floating-point format Divisor Aerodynamics Multiplication Social class Family Window Level of measurement Decision theory View (database) Counting Attribute grammar Nominal number Bit Binary file Subgroup Partition (number theory) Subset Type theory Frequency Personal digital assistant Network topology Website Software testing Condition number Object (grammar) Mathematical optimization Form (programming) Spacetime
Group action Decision theory Student's t-test Mereology Attribute grammar Bit rate Different (Kate Ryan album) Number theory Moving average Physical law Maize Multiplication Library (computing) Multiplication Interior (topology) Attribute grammar Staff (military) Student's t-test Binary file Continuous function Personal digital assistant Uniform resource name Order (biology) Condition number Software testing
Decision theory View (database) Price index Online help Morley's categoricity theorem Emulation Number Attribute grammar Measurement Revision control Heegaard splitting Single-precision floating-point format Energy level Physical law Vertex (graph theory) Information Social class Information Cellular automaton Attribute grammar Student's t-test Measurement Flow separation Word Object (grammar) Game theory Quicksort
Divisor Multiplication sign Decision theory Decision tree learning Mereology Emulation Element (mathematics) Subset Neuroinformatik Frequency Medical imaging Heegaard splitting Pi Blog Different (Kate Ryan album) Object (grammar) Set (mathematics) Entropie <Informationstheorie> Information Inductive reasoning Social class Physical system Priority queue Metropolitan area network Multiplication Scaling (geometry) Information Closed set Attribute grammar Multilateration Element (mathematics) Type theory Uniform resource name Network topology Order (biology) Social class Game theory
Point (geometry) Email Group action Divisor Logarithm Decision theory Rule of inference Emulation Neuroinformatik Number Heegaard splitting Goodness of fit Different (Kate Ryan album) Hypermedia Negative number Authorization Entropie <Informationstheorie> Hardware-in-the-loop simulation Physical law Information Position operator Information Cellular automaton Computer Coma Berenices Attribute grammar Total S.A. Subgroup Partition (number theory) Personal digital assistant Lie group Uniform resource name Factory (trading post) Order (biology) Website Social class Game theory Row (database)
Group action Perfect group Logarithm Multiplication sign Decision theory Archaeological field survey Mereology Attribute grammar Neuroinformatik Heegaard splitting Network topology Personal digital assistant Physical law Information Maize Inductive reasoning Social class Metropolitan area network Information Decision theory Attribute grammar Total S.A. Partition (number theory) Arithmetic mean Personal digital assistant Heegaard splitting Condition number Social class Sinc function
Point (geometry) Group action State of matter Decision theory Decision tree learning Electronic mailing list Function (mathematics) Disk read-and-write head Mereology Power (physics) Attribute grammar Heegaard splitting Goodness of fit Bit rate Hypermedia Personal digital assistant Ideal (ethics) Software testing output Inductive reasoning Metropolitan area network Social class Area Rule of inference Matching (graph theory) Information Decision theory Attribute grammar Bit Student's t-test Parity (mathematics) Similarity (geometry) Particle system Prediction Personal digital assistant Function (mathematics) Network topology Sheaf (mathematics) Order (biology) output Social class Condition number Game theory Cycle (graph theory) Routing Inductive reasoning Wide area network
Point (geometry) Email Group action Personal identification number Service (economics) Divisor Logarithm Decision theory Multiplication sign Range (statistics) 3 (number) Student's t-test Discrete element method Hand fan Emulation Neuroinformatik Authorization Physical law Information output Addressing mode Summierbarkeit Social class Identity management Information Decision theory File format Structural load Moment (mathematics) Interior (topology) Computer Attribute grammar Student's t-test Demoscene Personal digital assistant Social class Game theory
Decision theory Decision theory Multiplication sign Range (statistics) Attribute grammar Student's t-test Student's t-test Open set Discrete element method Emulation Spring (hydrology) Process (computing) Entropie <Informationstheorie> Physical law Condition number Social class Game theory Musical ensemble World Wide Web Consortium
Building State of matter Decision theory Range (statistics) Set (mathematics) Branch (computer science) Student's t-test Open set Discrete element method Special unitary group Field (computer science) Emulation Subset Neuroinformatik Bit rate Network topology Physical law Software testing Area Metropolitan area network Decision theory Greatest common divisor Counting Attribute grammar Bit Student's t-test Multilateration Personal digital assistant Function (mathematics) Uniform resource name Network topology Phase transition Condition number Quicksort Game theory Musical ensemble Resultant Inductive reasoning Row (database)
Mobile Web Rule of inference Information Decision theory Decision theory Multiplication sign Computer Attribute grammar Student's t-test Mereology Rule of inference Neuroinformatik Attribute grammar Heegaard splitting Root Prediction Network topology Video game Social class Game theory Form (programming)
Presentation of a group Group action Decision theory Multiplication sign Coroutine Branch (computer science) Average Mereology Rule of inference Likelihood function Semantics (computer science) Computer icon Neuroinformatik Heegaard splitting Network topology Set (mathematics) Software testing Noise Information security Physical system Information Decision theory Outlier Cellular automaton Constructor (object-oriented programming) Bit Sample (statistics) Commitment scheme Network topology Order (biology) Interpreter (computing) Website Curve fitting
Algorithm Decision theory Decision theory Multiplication sign Discrete group Constructor (object-oriented programming) Attribute grammar Thresholding (image processing) Continuous function Measurement Flow separation Emulation Replication (computing) Sequence Partition (number theory) Subject indexing Network topology Network topology Different (Kate Ryan album) Set (mathematics) Physical law Information Routing
Purchasing Axiom of choice Metropolitan area network Multiplication sign Computer Control flow Counting Hypothesis Computer Hypothesis Neuroinformatik Type theory Goodness of fit Type theory Prediction Website Physical law Smoothing Object (grammar) Endliche Modelltheorie Bayesian network Condition number
Trail Randomization Multiplication sign Set (mathematics) Numbering scheme Student's t-test Counting Computer Neuroinformatik Number Subset Inclusion map Type theory Bit rate Single-precision floating-point format Estimation Smoothing Conditional probability Physics Condition number Social class Moment (mathematics) Computer Total S.A. Hypothesis Prediction Bayesian network Thermal conductivity
Series (mathematics) Trail Statistics Information Multiplication sign Computer Student's t-test Subset Neuroinformatik Thomas Bayes Arithmetic mean Event horizon Bit rate Calculation Theorem Software testing Software testing Musical ensemble Logic gate Condition number
Observational study Multiplication sign Decision theory Event horizon Code Neuroinformatik Attribute grammar Independence (probability theory) Frequency Thomas Bayes Bit rate Row (database) Pressure Condition number Boss Corporation Multiplication Physical law Computer Independence (probability theory) Attribute grammar Statistics System call Demoscene Arithmetic mean Estimation Oval Personal digital assistant Uniform resource name Network topology Order (biology) Social class Negative number Musical ensemble Game theory Bayesian network Row (database)
Personal identification number Observational study Multiplication sign Network switching subsystem Computer Sound effect Independence (probability theory) Attribute grammar Coma Berenices Online help Student's t-test Student's t-test Control flow Neuroinformatik CAN bus Personal digital assistant Set (mathematics) Physical law Right angle
Point (geometry) Decision theory Calculation 3 (number) Mereology Distance Neuroinformatik Number Attribute grammar Independence (probability theory) Thomas Bayes Mathematics Term (mathematics) Data Encryption Standard Set (mathematics) Negative number Row (database) Noise Position operator Pressure Multiplication Point (geometry) Computer Attribute grammar Total S.A. Basis <Mathematik> Student's t-test Incidence algebra Control flow Principle of maximum entropy CAN bus Estimation Personal digital assistant Network topology Theorem Video game Social class Software testing Negative number Bayesian network
Information management Support vector machine Line (geometry) Correspondence (mathematics) Decision theory Virtual machine 1 (number) Maxima and minima Dimensional analysis Power (physics) Number Neuroinformatik Optical disc drive Flow separation Bit rate Term (mathematics) Set (mathematics) Negative number Representation (politics) Linear map Hyperplane Task (computing) Real number Moment (mathematics) Sampling (statistics) Mereology Binary file Array data structure Vector space Personal digital assistant Network topology Linearization Social class Whiteboard Representation (politics) Hyperplane Task (computing)
Point (geometry) Time zone Computer file Assembly language Support vector machine Line (geometry) Multiplication sign Point (geometry) 1 (number) Distance Array data structure Flow separation Term (mathematics) Set (mathematics) Endliche Modelltheorie Boundary value problem Task (computing)
Point (geometry) Group action Clique-width Support vector machine Virtual machine Maxima and minima Set (mathematics) Shape (magazine) Mathematics Computer configuration Vector graphics Utility software Linear map Social class Point (geometry) Maxima and minima Line (geometry) Electric power transmission Flow separation Uniform boundedness principle Array data structure Angle Personal digital assistant Game theory Whiteboard Session Initiation Protocol
Point (geometry) Maxima and minima Division (mathematics) Planning Distance Number Type theory Fraction (mathematics) Error message Vector graphics Social class Right angle Hyperplane Metropolitan area network Spacetime
Point (geometry) Linear equation Slide rule 12 (number) Constraint (mathematics) Distance Shift operator Scalability Computer icon Array data structure Causality Computer configuration Set (mathematics) Energy level Row (database) Hyperplane Linear map Matching (graph theory) Real number Point (geometry) Planning Line (geometry) Type theory Array data structure Scalar field Telecommunication Right angle Quicksort Object (grammar) Hyperplane Physical system Spacetime
Trail Logical constant Scaling (geometry) Divisor Equals sign Direction (geometry) Multiplication sign Parallel port Insertion loss Line (geometry) Distance Shift operator Number Prime ideal Mathematics Scalar field Vector graphics Social class Boundary value problem Hyperplane
Equals sign Orientation (vector space) Sheaf (mathematics) Maxima and minima Limit (category theory) Shift operator Hypercube Neuroinformatik Prime ideal Mathematics Term (mathematics) Hyperplane Linear map Social class Data type Shift operator Logical constant Sound effect Maxima and minima Data mining Type theory Object (grammar) Hyperplane Mathematician Spacetime
Point (geometry) Beat (acoustics) Interior (topology) Constraint (mathematics) Equaliser (mathematics) Sheaf (mathematics) Maxima and minima Heat transfer Coordinate system Distance Mereology Special unitary group Spacetime Physical law Hyperplane Physical system Constraint (mathematics) Slide rule Clique-width Moment (mathematics) Coordinate system Distance Algebra Order (biology) Linearization Right angle Object (grammar) Hyperplane
Point (geometry) Standard deviation Constraint (mathematics) Moment (mathematics) Maxima and minima Bit Maxima and minima Electronic mailing list Root Divisor Mathematical optimization Square number Form (programming)
Point (geometry) Metropolitan area network Implementation Constraint (mathematics) Support vector machine Constraint (mathematics) Direction (geometry) Virtual machine Set (mathematics) Maxima and minima Extreme programming Funktionalanalysis Computer programming Neuroinformatik Derivation (linguistics) Term (mathematics) Uniform resource name Nichtlineares Gleichungssystem Quicksort Procedural programming Data structure Library (computing)
Divisor Support vector machine Multiplication sign Virtual machine Set (mathematics) Disk read-and-write head Total S.A. Value-added network Neuroinformatik Programmer (hardware) Latent heat Personal digital assistant Set (mathematics) Diagram Error message Multiplication Social class Physical system Matching (graph theory) Fitness function Total S.A. Maxima and minima Line (geometry) Type theory Error message Linearization Social class Iteration
Point (geometry) Support vector machine Texture mapping Multiplication sign Online help Insertion loss Regular graph Mereology Twitter Medical imaging Flow separation Set (mathematics) Spacetime Software testing Circle Testmenge Data type Addition Cellular automaton Weight Planning 3 (number) Mereology Oval Order (biology) Curve fitting Resultant Spacetime
Algorithm Decision theory Multiplication sign Characteristic polynomial Virtual machine Maxima and minima Distance Rule of inference Event horizon Emulation Subset Neuroinformatik Independence (probability theory) Heegaard splitting Facebook Goodness of fit Degree (graph theory) Roundness (object) Different (Kate Ryan album) Term (mathematics) Physical law Information Algebra Data type Electric generator Information Decision theory Block (periodic table) Maxima and minima Linear algebra Binary file Cartesian coordinate system Array data structure Error message Entropie <Informationstheorie> Network topology Calculation Linearization Object (grammar) Mathematical optimization Routing
Subject indexing Algorithm Mathematical analysis
Boys like the idea so welcomed Welcome to are lecture We list of right in the middle so somewhere in the part that a where housing stand when the popular mining now a solo last week low sequence had and mining movement
Yes last week with the money and a room and and were talking about about time series of and that that is collected over some time-spans be exploited for getting new knowledge getting new information out of the death toll at Trent and the restrictions and the all of that kind of stuff and I'm not quite sure if you look at some of the video so for all the people that could be with us for the 1st public electorate and there are some videos on the cost website looking sell and you'll might want to re fresh knowledge about where housing and the mining but she believed use was of something and if there any questions about the and I will be happy to go on all the questions that you might have thought the plan for the future he will be staying with us and the lecture and the new of Brooke lecture with with the PM think that of all the things 2 months to mount a sign fellow you can pick the right time spent free not the at the moment and that it was communities and we had a lovely that basically of this kind of happy with the great great can rather than going out with state that and then take buying and in summer the faulty the nicest said time of the year and brought by up at least again How with some nice lectures and the where housing 1 all of the mining 1 that you might particular like because it's very helpful in the Ministry
And this is what they care about So there today won't talk about classification for a little while and basically we will look into 3 major techniques for classification the 1st decision trees and then we have night based classifications and the last 1 which was 1 of the most model in 1 of supply the machines works well and we will go into the house for the night after the Blue because we lost the 1st 4 holes to the muscles and and anyway
The basic point is was classification at the idea is that you a collection of record whatever may be maybe trans actions from from your cash registered may be or other kind of data so production that some of some ministers the and the each record belongs to 1 or more losses Class is semantic characteristic that is shared by some of the records of was usually called off at so you just named save all people who are male on the people will female upon the 2 classes of people where each person is a record of and this is what you do in classification and what I want to do is use a while basically it Antonellis somebody's male female after look at him or her in the neck and site Without asking the most cases are you that your Mayo see that you are a man and see that the female of but however recognise It is something that the well basically take other at tribute into account but maybe some old state heuristics also for example women tend to have longer which Trulia wrong over his head but 2 out of 3 is not too bad for the mayor's slowly of what all the dead about in the book the only ones are quite sure owner all of also wrong so you see that its heuristics is basically you take some of the attributes of the conceit and then you try to match it would was know about typical Types of the and the moral it kind of gets into a picture of the bed a classification will be so many basically Dubai is you build a class at tribute or you define class at tribute as being a function of the characteristics that you know of the tree that you know of a case and when everyday and Countess of move record Dosunmu entity you need to sign this year entities to some of the losses as accurately as possible so we know about of the is is used as and when he wanted to do 2 to show you what book cost and how the view that while basically it said it learning Elgar's are so what you do is you have tested by just saying OK he 100 people that tell you who have them is of of cloth and then you have to figure out However this decision whose in what could be made from and this is the notion of a test set up on the storey of this is the notion of the training set of post after trains my mobile my classified with some known entity and then not have tests that was a big call accurate is my design Sell many of the test case is are classified correctly hominy on the wrong assigned to some clubs with APEC as a basic the idea so you have set off objects you divide them into the Test side and the training sent the training sent to train them all And then with the test you test the model And everything for another acuity and then you can come reiterated take new training set of test that I'm
Now the question of whether this is houses useful be useful and of the the 1st of and the 1st use was in the financial feel so what people wanted to know is that some processes of for example the approval process for loans somebody walks into a banque and tells you like I'm 54 user woollen Naidoo now houses and no 1 by his 2nd house and higher and so on so much money and like and will you give me a little Of 10 and somebody has to decide by not a gut feeling something that you have to have some some matches in which he to find only wanted to talk to democratique so are investigating some some some group of people still Eidinow 20 30 deals or something I find out on your product board by everybody only the 20 to 30 year old don't You can't this group widened by the road is not not valuable for them more than they just know about it all whatever it is now like to find out about there so I'm back recognition alike declassified credit card transaction of a legitimate dollar for will be the biggest expect them some Fault play in some of the usage and that is basically what you do with classification algorithms and this is why the very useful in the rain pulled thanks to a 2 noble and anyway But Yes said the good of saving and everybody's new to lecture happens once and while the the test safe some of the annotations that ideas splendidly said smashing anyway again how does it work as utterly or any other year trading said that trading said has a certain cloth label are saying by the something belongs to a class all the belong to a club or if you have a lot of club's so much about category to which category belong
And the racket so for example of this is a record over its as I'd analysis of some identification number and and just values for for some some some summer may be yes no values may be numbers may be whatever upon and these and other correct answer for each of them So why do it is a trying to figure out Powell with this decision is interrelated with the values of the assessed some kind of connexion between this is what I'd you which is basically a good model could of the model of everybody who has yet in at reviewed 1 is also Klaus X and everybody who has no need to do about it of class wife for example something way and the morale is trained offices higher by by some ruining Elbaz this he that different kinds of growth we will over a visit 3 of them the decision tree algorithms the causing knife based classifiers Paul victims and from that you have an instance of the model But of course you train with some some fixed set of that high would would for full with unknown and this is exactly what the test said is about because here The closet computer missing from I'd No 8 because I've taken it from the a usually trade taking part of training said as a test of the them before or training by more but the algorithm doesn't know So the algorithm comes up with a conjecture And then icons knowing the origin of the use of the test Determined correctness of my classified is as good model officially good put the that model does totally under the picked up and that's a basic idea behind
But that's going to example also we have talked about credit approval for the 1st step will be learning how I'd have some records the and are already know that the decision was basically by sit next to the banque clerk and look at the people walking and and see what written down you are like so many among the income and 1 of the club's interested in them the clerk rakes the decision And I'd just note the class at computer science Icollector say 500 all thousand cases in the memory And then I'd have This table which basically much OK The use classification algorithm the and Anderton some hope what happened in the head of the And put it into classification route so for example if somebody is younger than the decision should always be a risk because those people come to work with money and but I don't own anything that worst mentioning and stuff like the every round may be a good decision if UK income as high than the decision say so this kind of rule gives me a way to to deal was unknown because it does not reflect on the decisions made by just the income visible at the age is young Stuff like that can sometimes several of them
2nd step is the classification have my classification room and now to new debt For example Henry middle low known decision but the wrote it income as though that allways risky so risk and this is basically this is what we want of cost the intelligence is he he Howard like at classification rules out of the Test How effective is that But and this is what we are going to look into intimate
The best of the 3 kind of learning about wanted the civilised during in supervised learning the training that comes with all the clubs labels and the new data that again is classifies based on the findings If the simplest learning this also uneasy robust learning and supplies to and included tricky where there are no labels and the training set so we have to find out by some of origin methods class tringle 1 of how the training said can be group into sensible units can stop The Class labels are basically unknown and that is basically the idea of class during which will reflected in next week's lecture of their after in will be the biggest pub today Daewoo deal was only suppressed learning so we know the class labels and in the training set and have to figure out how to build a good classification I'm that has so far possibilities so called semi supervised remain where some of you don't some but animal to go into the to to the bomb will just over who was to enable the justification case on the unsupervised yearning for the class of the world
That the prominent classification techniques on 1 hand decision Tree witches 30 popular because it but it yields exactly those those classification groups and the same holds for the full through Rubens's were just state some simple rules like somebody young in the long application Chable was rejected by a 2nd possibility is a up probabilistic approach with is that the days of Asian and and the board and the last so far complex long circles pulled back to machines which would go into at the end of lectures and the last 1 the new works that especially tricky we won't go into that too deeply here because it's it's kind of like nobody really knows what you but that it has some training some rules and but and can't classified actually of most of the new owner can justify pretty effective but you don't get to be about the nature of the rules of to the only know about how the network looks so that does not help you to much and well defined decision 1 good way to think the 1st launched was today is the decision trees and the decision tree is basically a float childlike like tree structures are you stopped it be the at the root of and every note that you have with a new treaty is basically test on some attribute
The for example the root of all was the H And some every note the branches we of edges and the graphs That tell you some Restriction on the edge of the sofa somebody age note the Brahms might be smaller than 30 31 to fully large of the board which is basically how split up all possibilities of of having the h t fight on deaf costs they should be this joint doesn't really purity of several possibilities to go because of somebody tells you age should have exactly 1 Brahms's to go into the next this is a path that the snow notes Other decisions itself out last labels that could meet a pen And while internodes Monday's notes always new tests so that helps you basically depending on body of and on when you get as announced from some into a note It might have different possibilities of asking new question so for example if you find out that somebody is a between 31 and It was a good candidate for loan no further questions all of or you find out that somebody has some of the high income And once a small amount of money to 1 of the Just handed out That I many questions that is basically basically the and the idea but them had we did how to find out about this she but the decision Tree looked like because we don't have the tree we of the training that with the right at the top flight looking more nothing and them many algorithms actually doing that of 1 of the earliest methods that will go into in just a minute is Hunt's algorithm albeit of the best known method of the wrong this that has some of the mood Bradley in existence and and the used and at this moment as a sea for 5 of the very real known technique is also done which will goal not to deep into because of what but complex and and the basic idea is very similar to hunt Elgar's sole where want to look at it is a welcome
Then there was classification regression trace the call up algorithm also 1 of the top that mining algorithms and their interest in some of the techniques that we would go into 2 deeply here
So basically A want talk about Hunt's Algorithm for moment and the general structure is that you have a set of training record notified the chief that data to reach some note of and what I do it is used split the status By the classes the different that it belongs to And And The knowledge That has to be defined Is he the class label if the old items and that had already been on the same group Has no point of splitting the Macau In the other cases Whether be long to different Plus a gift to split them Such lack Objects with different classes full into different groups That is not where always possible was the singles that might need more If so tried to break free a big part of so rather split and and equal parts were where most of the of the city of the funds at the end So If a single split doesn't help you in Italy classifying that correctly if to apply recursively with the new debt is said to find new note Split into a different classes and look what happened that example from last year is records about
People getting tax refunds or so this The are tax records and you have the idea of somebody America's status the information whether he or she got refund or not and that the information held by the those Leruo of the year the cup and he of the another Interesting column year that is This year she cheat on not the world we know that some people are so I'm not quite sure how he would know that they didn't she may be checked and it did find something and interesting maybe they hate that cheating so well that he could find it added that had system for a minute that will be a serious classes When you do it you 1st The find some attribute Up and the SAS for example refund Beckham's play but that was the biggest in the nose of the UK and so getting the information about the refund which to to possible Comes yes on of the disparate at a refund but the of has personal refund now that look at the classes the we find so for example we have a yes here She now we have to get the she now where the here she now The know that you got a refund in my point as she could Identity to split this further because of well single cost label A Kansas City well if somebody did get a refund What about the other alternative to black nodal Fowey of the No he'd you the you new and we have been Niall big for the all the different classes of display them again So the populist up the standard look at some of the other 2 being so for example we will focus on Commercial status Well the people who say no of new wiped out from So we are looking at But no you The no here And the Nokia And the thought of But part of the game because they are in this branch of the and not interested in them So far all the way do I do we find out 2nd tribute to want to know about the marital status of Kent For the last were written to find something so we have a mandate 1 he let me do it and Blue American American here American And all the others the singled and the walls how about what doesn't work is no is known as a no of this the Oriental American people which got no refund 32 The decathlete like up and will have to to split the for the 1 about the other side is the only way to yes the guests who were no bad Here we some Get this part by looking at the West well the loss to the yes the singer has also yes and the singers as and so that the mouth of a three way split would not so that put the single and the Boston 1 set up and and we have to work again with the winner work with the refund we now work with America's status still won to achieve left that some sense of Bob me Of this up 4 minutes from named Or Kent Service singers and you will also need the singles and the Bulls who did not get a refund The as a single At The city will also Not a refund The single And another finger Oka You're The solar cell cases are in this book stony and Let's find out how quickly and but them by the taxable income 70 gave 4 1 0 For every ILO's Have higher income Boca Some Smolen 70 gate But chic They don't teach which was the no he of a pet For all all the others that she does What do we get out of this Opie Bill would not get in the way of a refund Or single the that means have time on their hand and have a high income about to cheat next time of The basic idea of ministers are part of what is some some questions and mid to gritty because an order to Noel was a refund them and look at the mercy of the had to the basic for where some heuristics but 1 might
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Looks like this You can by them in the early and even worse if you put something through the air what happens if the loyalty of misconduct acidification so even though I'm a soft mountain classified don't help you because of a lack of Everest he The from him But enough about them to do what I can do is you can skip the Linear upon and just go to a higher demand And saying what if my classified what comic classified look like that The and houses basically down this is basically down by going to the 3rd time engine way you have parabolic Classified which easy to be fund with freedom and and then he you cut through it For the plate And the protection offers on the plane to be circle can you take the trouble from Space make up for it to end up with some of the affected her account so you can use higher them engine justified and against naggin the implicated But basically using high enough time engine You can get any you like into the image of the A could really get something like this Greenline down here just by moving up the couple spaces and accounting for Kent should you that Toseland complicated enough were probably not the because and the Tony before or if we have an order Lahia building such a separated is possible T It is possible that what happens if some Red Book her he would be very bad for the occasion but the perfectly legal perfectly developed point for some for some Redbook to appeal for calm So the high I'd in the demand mentioned less robust the singing utterly that 1 of the major Wantage's of above a committee is the robustness and the and the like trade off again and again fitting is kind of like a very difficult thing to controlled and your with have to make sure that you don't want into a fitting usually this is also done with tests at the cell and is a perfect justification for the Trading said reach that is something that is not to say that it might take 2 downright badly to new items because you get to special again perfectly fit the Training said that was cost and and what I usually do is kind of across addition you split the available data into positronic that and test sent
And then you find a solution for the training such and Test with a test of whether their loss to talk that 2 of fitted over that works and so I use 1 part for she during the classified which is the 2nd part of checking the classified and you try some bad classification will some slightly was classification again compute the results of trained of the tests that and then take the classified thoughtful that has the best of homes tests on the trend again Across ability but that regularization if you know how good classifed roughly should look like though though it is usually possible to divide the said Llanelli see many can also introduced Kennedy values into the Optimization from for skipping damage to lose the weight loss to the if you off all way from what you would expect you have a lot and the if you are quite close to what you would expect semantically speaking to incur only no penalty and you take the 1 minimize you your penalty and example such a regularization of mountain technique which accepted that he you can not separated the said properly but you incur some was still to punish the the area and that while the to fall from from the mountain
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Sell to they would talk about classification the basic the 3 kind but we wanted to talk about the decision trees and with the times as well you use the inflammation gained by looking at what splitting characteristics you can find to level you find you set into into smaller subset that he to some label and you directly can generate classification rules from that in the terms of questions if somebody is Young and the late of a block the and he is able to buy a computer or a 2nd round based classification walls probabilistic happy arrived probabilities for The event that somebody by computer or doesn't not by making it night few gets too easy calculation and you extract from statistical depend on the other hand But he faced father very robust global might be a good idea and surfing with talked about their with OPEC to to machines the of fine reclassification techniques that usually works and Linear Facebook and the St or to use a more complex places the idea was to find a maximum Object classified kind of maximised as the distance between the 2 Different different itemsets and all of the different declassified and that and then chooses separated between basically prop of linear algebra so we have seen 3 approaches 1 published a could approach on approach from India algebra and took 1 approach based on the information Dudley different approaches for come to correct classification of the dead Depends on the application which use and which easy beneath the route 3 need something that is very robust and 3 after missing dad and for all that it might be that the public pros and cons and you can just chop around with the idea Questions Ever since Kent understood the principle of classified Bucket Next
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