Upload File

cs 188 section 12 - daylen · other classifiers discussed nearest neighbors parametric /...

  • Home
  • Documents
  • CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings
10
CS 188 SECTION 12 These slides are on Piazza! Search for “Daylen’s slides”

Upload: others

Post on 13-Jul-2020

2 views

Category:

Documents


0 download

Report

TRANSCRIPT

Page 1: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

CS 188 SECTION 12These slides are on Piazza! Search for “Daylen’s slides”

Page 2: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

CORRECTION: LAPLACE SMOOTHINGLaplaceSmoothing

▪ Laplace’sestimate(extended):▪ Pretendyousaweveryoutcomekextratimes

▪ What’sLaplacewithk=0?▪ kisthestrengthoftheprior

▪ Laplaceforconditionals:▪ Smootheachconditionindependently:

r r b

Number of events that X can take on

Page 3: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

CALCULUS REVIEW SECTIONS

➤ Session 1: today; 6-7:30 pm, Soda 405: single variable calculus

➤ Session 2: today; 7:30-9 pm, Soda 405: identical content to session 1

➤ Session 3: tomorrow; 6-7.30 pm, Soda 380; multi variable calculus

➤ Session 4: tomorrow; 7.30-9 pm, Soda 380; identical content to session 3

Page 4: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

UPCOMING DEADLINES

➤ Project 5 due today @ 5pm

➤ HW 6 due Wednesday @ 11:59

➤ Project 6 due Sunday @ 5pm

➤ Final Exam next Thursday

Page 5: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

BINARY PERCEPTRONSLinearClassifiers

▪ Inputsarefeaturevalues▪ Eachfeaturehasaweight▪ Sumistheactivation

▪ Iftheactivationis:▪ Positive,output+1▪ Negative,output-1

Σf1f2f3

w1w2w3

>0?

Page 6: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

BINARY PERCEPTRONS Learning:BinaryPerceptron

▪ Startwithweights=0▪ Foreachtraininginstance:▪ Classifywithcurrentweights

▪ Ifcorrect(i.e.,y=y*),nochange!▪ Ifwrong:adjusttheweightvectorbyaddingorsubtractingthefeaturevector.Subtractify*is-1.

Page 7: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

MULTICLASS PERCEPTRONS

MulticlassDecisionRule

▪ Ifwehavemultipleclasses:▪ Aweightvectorforeachclass:

▪ Score(activation)ofaclassy:

▪ Predictionhighestscorewins

Binary=multiclasswherethenegativeclasshasweightzero

Learning:MulticlassPerceptron

▪ Startwithallweights=0▪ Pickuptrainingexamplesonebyone▪ Predictwithcurrentweights

▪ Ifcorrect,nochange!▪ Ifwrong:lowerscoreofwronganswer,

raisescoreofrightanswer

MulticlassDecisionRule

▪ Ifwehavemultipleclasses:▪ Aweightvectorforeachclass:

▪ Score(activation)ofaclassy:

▪ Predictionhighestscorewins

Binary=multiclasswherethenegativeclasshasweightzero

Page 8: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

OTHER CLASSIFIERS DISCUSSED

➤ Support Vector Machines

SupportVectorMachines

▪ Maximizingthemargin:goodaccordingtointuition,theory,practice▪ Onlysupportvectorsmatter;othertrainingexamplesareignorable▪ Supportvectormachines(SVMs)findtheseparatorwithmaxmargin▪ Basically,SVMsareMIRAwhereyouoptimizeoverallexamplesatonce

MIRA

SVM

SupportVectorMachines

▪ Maximizingthemargin:goodaccordingtointuition,theory,practice▪ Onlysupportvectorsmatter;othertrainingexamplesareignorable▪ Supportvectormachines(SVMs)findtheseparatorwithmaxmargin▪ Basically,SVMsareMIRAwhereyouoptimizeoverallexamplesatonce

MIRA

SVM

Page 9: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

OTHER CLASSIFIERS DISCUSSED

➤ Nearest Neighbors

Parametric/Non-Parametric

▪ Parametricmodels:▪ Fixedsetofparameters▪ Moredatameansbettersettings

▪ Non-parametricmodels:▪ Complexityoftheclassifierincreaseswithdata▪ Betterinthelimit,oftenworseinthenon-limit

▪ (K)NNisnon-parametric Truth

2Examples 10Examples 100Examples 10000Examples

Page 10: CS 188 SECTION 12 - Daylen · OTHER CLASSIFIERS DISCUSSED Nearest Neighbors Parametric / Non-Parametric Parametric models: Fixed set of parameters More data means better settings

WORKSHEET


Personalized classifiers

5 classifiers

Separating and Classifiers

Nearest Neighbourhood Classifiers in Biometric Fusion · Nearest Neighbourhood Classifiers in Biometric Fusion Nearest Neighbourhood Classifiers in Biometric Fusion ... Electronic

Lecture8 classifiers ldc_rules

PARAMETRIC AND NON-PARAMETRIC SIGNAL ANALYSIS FOR …faculty.nps.edu/ravi/My Papers/ICASSP_5_04.pdf · 2004-02-19 · The non-parametric signal classifiers include non-linear alignment

8. Classifiers

Classifiers Notes

Learning Bayesian Classifiers

Machine Learning Classifiers

Linear Classifiers/SVMs

Perceptron Linear Classifiers

Linear Classifiers

KNN & Naïve Bayes Hongning Wang CS@UVa. Today’s lecture Instance-based classifiers – k nearest neighbors – Non-parametric learning algorithm Model-based

Gaussian Bayes Classifiers

Decision Boundary Feature Extraction for Non …landgreb/SMC93.pdfprincipal component analysis might be used for non-parametric classifiers, those methods generally do not find optimum

Lecture 12 Classifiers Part 2 Topics Classifiers Maxent Classifiers Maximum Entropy Markov Models Information Extraction and chunking intro Readings: Chapter

More Classifiers

1.1. Nearest-Neighbour Classifiersetheses.whiterose.ac.uk/17976/3/Part II.pdf · 2017-08-10 · 3 learning-based parametric classifiers. Because prior training is necessary, their

5 character classifiers

Comparison of Classifiers

Naïve Bayes Classifiers

Ideology Classifiers for Political Speech Stefan Kaufmannccc.inaoep.mx/~villasen/bib/Ideology Classifiers for Political Speech.pdf · Ideology classifiers for political research pg

Reflux Classifiers

Speech Recognition Pattern Classification. 22 September 2015Veton Këpuska2 Pattern Classification Introduction Parametric classifiers Semi-parametric

Classifier Ensemble for Improving Land Cover Classification · such parametric classifiers to handle complex datasets consisting of different kind of data such as multisource data

Speech Recognition Pattern Classification. 4 December 2015Veton Këpuska2 Pattern Classification Introduction Parametric classifiers Semi-parametric

8. Classifiers - Max Planck Societypubman.mpdl.mpg.de/.../component/escidoc:1582926/Classifiers.pdf · 8. Classifiers 1. Introduction 2 ... and examples of classifiers in various

8. Classifiers - MPG.PuRe

Kazuya Akimoto Piet Mondriaan. Salvador Dalí Non-parametric non-linear classifiers Willem Melssen [email protected] Institute for Molecules and

Boosting of classifiers

Classifiers & Classification - School of Computer …€¦ · Computer Vision - Lecture 12 2 Lecture Overview An introduction to Classifiers Parametric and Non-parametric approaches

Comparison of Segment and Pixel Based Non-Parametric … · 2020-01-21 · the respective non-parametric classifiers, multitemporal bands were chosen in 38 of the 44 total classifications,

Evaluating Classifiers

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND ...akghosh/IEEE-TPAMI-2009.pdfParametric and Nonparametric Classifiers Probal Chaudhuri, Anil K. Ghosh, and Hannu Oja Abstract—Parametric