SlideShare une entreprise Scribd logo

Ch 9-1.Machine Learning: Symbol-based

•Télécharger en tant que PPT, PDF•
•
B
butest
1  sur  34
Ch 9. Machine Learning: Symbol-based ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.0 Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.0 Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Any change in a system that allow it to perform better the second time on repetition of the same task or on another task drawn form the same population (Simon, 1983)
9.0 Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.0 Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.0 Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.1 Framework for Symbol-based Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A general model of the learning process (Fig. 9.1)
9.1 Framework for Symbol-based Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.1 Framework for Symbol-based Learning
9.1 Framework for Symbol-based Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],size(obj1, small) ^ color(obj1, red) ^ shape(obj1, round) size(obj2, large) ^ color(obj2, red) ^ shape(obj2, round) => size(X, Y) ^ color(X, red) ^ shape(X, round)
9.1 Framework for Symbol-based Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.1 Framework for Symbol-based Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Examples and Near Misses for the concept “Arch” (Fig. 9.2)
Generalization of descriptions (Figure 9.3)
Generalizations of descriptions (Fig 9.3 continued)
Specialization of description (Figure 9.4)
9.2 Version Space Search ,[object Object],[object Object],[object Object],[object Object]
9.2.1 Generalization Operators and the Concept Spaces ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.2.1 Generalization Operators and the Concept Spaces ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A Concept Space (Fig. 9.5)
9.2.2 The candidate elimination algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.2.2 The candidate elimination algorithm ,[object Object],[object Object],[object Object],A concept c, is maximally specific if it covers all positive examples, none of the negative examples, and for any concept c’, that covers the positive examples, c  c’ A concept c, is maximally general if it covers none of the negative training instances, and for any other concept c’, that covers no negative training instance, c  c’.
Specific to General Search
Specific to General Search (Fig 9.7)
General to Specific Search
General to Specific Search (Fig 9.8)
9.2.2 The candidate elimination algorithm
9.2.2 The candidate elimination algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.2.2 The candidate elimination algorithm (Fig. 9.9)
9.2.2 The candidate elimination algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
9.2.2 The candidate elimination algorithm
9.2.2 The candidate elimination algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object]
9.2.4 Evaluating Candidate Elimination ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Recommandé

Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-basedCh 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-basedbutest
 
Ai inductive bias and knowledge
Ai inductive bias and knowledgeAi inductive bias and knowledge
Ai inductive bias and knowledgeDexter Montesclaros
 
Machine Learning
Machine LearningMachine Learning
Machine LearningProf. Dr. K. Adisesha
 
Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Syed Atif Naseem
 
Text categorization
Text categorizationText categorization
Text categorizationPhuong Nguyen
 
Vc dimension in Machine Learning
Vc dimension in Machine LearningVc dimension in Machine Learning
Vc dimension in Machine LearningVARUN KUMAR
 
20070702 Text Categorization
20070702 Text Categorization20070702 Text Categorization
20070702 Text Categorizationmidi
 
Analytical study of feature extraction techniques in opinion mining
Analytical study of feature extraction techniques in opinion miningAnalytical study of feature extraction techniques in opinion mining
Analytical study of feature extraction techniques in opinion miningcsandit
 

Recommandé

Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-basedCh 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-basedbutest
 
Ai inductive bias and knowledge
Ai inductive bias and knowledgeAi inductive bias and knowledge
Ai inductive bias and knowledgeDexter Montesclaros
 
Machine Learning
Machine LearningMachine Learning
Machine LearningProf. Dr. K. Adisesha
 
Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Statistical Pattern recognition(1)
Statistical Pattern recognition(1)Syed Atif Naseem
 
Text categorization
Text categorizationText categorization
Text categorizationPhuong Nguyen
 
Vc dimension in Machine Learning
Vc dimension in Machine LearningVc dimension in Machine Learning
Vc dimension in Machine LearningVARUN KUMAR
 
20070702 Text Categorization
20070702 Text Categorization20070702 Text Categorization
20070702 Text Categorizationmidi
 
Analytical study of feature extraction techniques in opinion mining
Analytical study of feature extraction techniques in opinion miningAnalytical study of feature extraction techniques in opinion mining
Analytical study of feature extraction techniques in opinion miningcsandit
 
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
 
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)hani_abdeen
 
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwiseLearning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwiseHasan H Topcu
 
SVM Tutorial
SVM TutorialSVM Tutorial
SVM Tutorialbutest
 
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...NTNU
 
The Gaussian Process Latent Variable Model (GPLVM)
The Gaussian Process Latent Variable Model (GPLVM)The Gaussian Process Latent Variable Model (GPLVM)
The Gaussian Process Latent Variable Model (GPLVM)James McMurray
 
Svm
SvmSvm
Svmwltongxing
 
A Theory of Scope
A Theory of ScopeA Theory of Scope
A Theory of ScopeLars Marius Garshol
 
17 Machine Learning Radial Basis Functions
17 Machine Learning Radial Basis Functions17 Machine Learning Radial Basis Functions
17 Machine Learning Radial Basis FunctionsAndres Mendez-Vazquez
 
（DL輪読）Matching Networks for One Shot Learning
（DL輪読）Matching Networks for One Shot Learning（DL輪読）Matching Networks for One Shot Learning
（DL輪読）Matching Networks for One Shot LearningMasahiro Suzuki
 
A Maximum Entropy Approach to the Loss Data Aggregation Problem
A Maximum Entropy Approach to the Loss Data Aggregation ProblemA Maximum Entropy Approach to the Loss Data Aggregation Problem
A Maximum Entropy Approach to the Loss Data Aggregation ProblemErika G. G.
 
A Note on Confidence Bands for Linear Regression Means-07-24-2015
A Note on Confidence Bands for Linear Regression Means-07-24-2015A Note on Confidence Bands for Linear Regression Means-07-24-2015
A Note on Confidence Bands for Linear Regression Means-07-24-2015Junfeng Liu
 
Template matching03
Template matching03Template matching03
Template matching03amitkhanna1991
 
Machine Learning 1 - Introduction
Machine Learning 1 - IntroductionMachine Learning 1 - Introduction
Machine Learning 1 - Introductionbutest
 
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearchJarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearchPalGov
 
Paper Summary of Disentangling by Factorising (Factor-VAE)
Paper Summary of Disentangling by Factorising (Factor-VAE)Paper Summary of Disentangling by Factorising (Factor-VAE)
Paper Summary of Disentangling by Factorising (Factor-VAE)준식 최
 
Generalization abstraction
Generalization abstractionGeneralization abstraction
Generalization abstractionEdward Blurock
 
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNING
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNINGRECENT ADVANCES in PREDICTIVE (MACHINE) LEARNING
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNINGbutest
 
Text categorization as graph
Text categorization as graphText categorization as graph
Text categorization as graphHarry Potter
 
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Marina Santini
 
ppt
pptppt
pptbutest
 
Figure 1.doc
Figure 1.docFigure 1.doc
Figure 1.docbutest
 

Contenu connexe

Tendances

ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
 
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)hani_abdeen
 
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwiseLearning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwiseHasan H Topcu
 
SVM Tutorial
SVM TutorialSVM Tutorial
SVM Tutorialbutest
 
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...NTNU
 
The Gaussian Process Latent Variable Model (GPLVM)
The Gaussian Process Latent Variable Model (GPLVM)The Gaussian Process Latent Variable Model (GPLVM)
The Gaussian Process Latent Variable Model (GPLVM)James McMurray
 
17 Machine Learning Radial Basis Functions
17 Machine Learning Radial Basis Functions17 Machine Learning Radial Basis Functions
17 Machine Learning Radial Basis FunctionsAndres Mendez-Vazquez
 
（DL輪読）Matching Networks for One Shot Learning
（DL輪読）Matching Networks for One Shot Learning（DL輪読）Matching Networks for One Shot Learning
（DL輪読）Matching Networks for One Shot LearningMasahiro Suzuki
 
A Maximum Entropy Approach to the Loss Data Aggregation Problem
A Maximum Entropy Approach to the Loss Data Aggregation ProblemA Maximum Entropy Approach to the Loss Data Aggregation Problem
A Maximum Entropy Approach to the Loss Data Aggregation ProblemErika G. G.
 
A Note on Confidence Bands for Linear Regression Means-07-24-2015
A Note on Confidence Bands for Linear Regression Means-07-24-2015A Note on Confidence Bands for Linear Regression Means-07-24-2015
A Note on Confidence Bands for Linear Regression Means-07-24-2015Junfeng Liu
 
Machine Learning 1 - Introduction
Machine Learning 1 - IntroductionMachine Learning 1 - Introduction
Machine Learning 1 - Introductionbutest
 
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearchJarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearchPalGov
 
Paper Summary of Disentangling by Factorising (Factor-VAE)
Paper Summary of Disentangling by Factorising (Factor-VAE)Paper Summary of Disentangling by Factorising (Factor-VAE)
Paper Summary of Disentangling by Factorising (Factor-VAE)준식 최
 
Generalization abstraction
Generalization abstractionGeneralization abstraction
Generalization abstractionEdward Blurock
 
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNING
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNINGRECENT ADVANCES in PREDICTIVE (MACHINE) LEARNING
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNINGbutest
 
Text categorization as graph
Text categorization as graphText categorization as graph
Text categorization as graphHarry Potter
 
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Marina Santini
 

Tendances (20)

ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MINING
 
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)
Multi-Objective Optimization in Rule-based Design Space Exploration (ASE 2014)
 
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwiseLearning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
 
SVM Tutorial
SVM TutorialSVM Tutorial
SVM Tutorial
 
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...
 
The Gaussian Process Latent Variable Model (GPLVM)
The Gaussian Process Latent Variable Model (GPLVM)The Gaussian Process Latent Variable Model (GPLVM)
The Gaussian Process Latent Variable Model (GPLVM)
 
Svm
SvmSvm
Svm
 
A Theory of Scope
A Theory of ScopeA Theory of Scope
A Theory of Scope
 
17 Machine Learning Radial Basis Functions
17 Machine Learning Radial Basis Functions17 Machine Learning Radial Basis Functions
17 Machine Learning Radial Basis Functions
 
（DL輪読）Matching Networks for One Shot Learning
（DL輪読）Matching Networks for One Shot Learning（DL輪読）Matching Networks for One Shot Learning
（DL輪読）Matching Networks for One Shot Learning
 
A Maximum Entropy Approach to the Loss Data Aggregation Problem
A Maximum Entropy Approach to the Loss Data Aggregation ProblemA Maximum Entropy Approach to the Loss Data Aggregation Problem
A Maximum Entropy Approach to the Loss Data Aggregation Problem
 
A Note on Confidence Bands for Linear Regression Means-07-24-2015
A Note on Confidence Bands for Linear Regression Means-07-24-2015A Note on Confidence Bands for Linear Regression Means-07-24-2015
A Note on Confidence Bands for Linear Regression Means-07-24-2015
 
Template matching03
Template matching03Template matching03
Template matching03
 
Machine Learning 1 - Introduction
Machine Learning 1 - IntroductionMachine Learning 1 - Introduction
Machine Learning 1 - Introduction
 
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearchJarrar.lecture notes.aai.2011s.ch3.uniformedsearch
Jarrar.lecture notes.aai.2011s.ch3.uniformedsearch
 
Paper Summary of Disentangling by Factorising (Factor-VAE)
Paper Summary of Disentangling by Factorising (Factor-VAE)Paper Summary of Disentangling by Factorising (Factor-VAE)
Paper Summary of Disentangling by Factorising (Factor-VAE)
 
Generalization abstraction
Generalization abstractionGeneralization abstraction
Generalization abstraction
 
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNING
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNINGRECENT ADVANCES in PREDICTIVE (MACHINE) LEARNING
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNING
 
Text categorization as graph
Text categorization as graphText categorization as graph
Text categorization as graph
 
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
 

En vedette

Figure 1.doc
Figure 1.docFigure 1.doc
Figure 1.docbutest
 
Monte -- machine learning in Python
Monte -- machine learning in PythonMonte -- machine learning in Python
Monte -- machine learning in Pythonbutest
 
Coaching para organizaciones
Coaching para organizacionesCoaching para organizaciones
Coaching para organizacionesIsabelSierra
 
SISTEM KOMUNIKASI DATA (SISKOMDAT)
SISTEM KOMUNIKASI DATA (SISKOMDAT)SISTEM KOMUNIKASI DATA (SISKOMDAT)
SISTEM KOMUNIKASI DATA (SISKOMDAT)Maya Ayunanda
 
This is a heavily data-oriented
This is a heavily data-orientedThis is a heavily data-oriented
This is a heavily data-orientedbutest
 
Torneo Lazzarotto
Torneo LazzarottoTorneo Lazzarotto
Torneo Lazzarottopitfigh
 
Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015
Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015
Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015KOSTAS LATHOURAS
 

En vedette (9)

ppt
pptppt
ppt
 
Figure 1.doc
Figure 1.docFigure 1.doc
Figure 1.doc
 
Monte -- machine learning in Python
Monte -- machine learning in PythonMonte -- machine learning in Python
Monte -- machine learning in Python
 
Coaching para organizaciones
Coaching para organizacionesCoaching para organizaciones
Coaching para organizaciones
 
SISTEM KOMUNIKASI DATA (SISKOMDAT)
SISTEM KOMUNIKASI DATA (SISKOMDAT)SISTEM KOMUNIKASI DATA (SISKOMDAT)
SISTEM KOMUNIKASI DATA (SISKOMDAT)
 
This is a heavily data-oriented
This is a heavily data-orientedThis is a heavily data-oriented
This is a heavily data-oriented
 
Torneo Lazzarotto
Torneo LazzarottoTorneo Lazzarotto
Torneo Lazzarotto
 
5k ser joven es...
5k ser joven es...5k ser joven es...
5k ser joven es...
 
Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015
Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015
Best oral presentation was awarded to Dr Kostas Lathouras - BIARGSNewsletter2015
 

Similaire à Ch 9-1.Machine Learning: Symbol-based

Ch 9-2.Machine Learning: Symbol-based[new]
Ch 9-2.Machine Learning: Symbol-based[new]Ch 9-2.Machine Learning: Symbol-based[new]
Ch 9-2.Machine Learning: Symbol-based[new]butest
 
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Transfer Learning for Improving Model Predictions in Highly Configurable Soft...
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Pooyan Jamshidi
 
Part 1
Part 1Part 1
Part 1butest
 
Introduction
IntroductionIntroduction
Introductionbutest
 
Introduction
IntroductionIntroduction
Introductionbutest
 
Introduction
IntroductionIntroduction
Introductionbutest
 
Machine Learning: Decision Trees Chapter 18.1-18.3
Machine Learning: Decision Trees Chapter 18.1-18.3Machine Learning: Decision Trees Chapter 18.1-18.3
Machine Learning: Decision Trees Chapter 18.1-18.3butest
 
17- Kernels and Clustering.pptx
17- Kernels and Clustering.pptx17- Kernels and Clustering.pptx
17- Kernels and Clustering.pptxssuser2023c6
 
PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...
PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...
PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...AkankshaRawat53
 
Surrogate modeling for industrial design
Surrogate modeling for industrial designSurrogate modeling for industrial design
Surrogate modeling for industrial designShinwoo Jang
 
Sample prac exam2013
Sample prac exam2013Sample prac exam2013
Sample prac exam2013hccit
 
Artificial Intelligence
Artificial Intelligence Artificial Intelligence
Artificial Intelligence butest
 
모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로
모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로 모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로
모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로 r-kor
 
Machine learning (5)
Machine learning (5)Machine learning (5)
Machine learning (5)NYversity
 
Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...
Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...
Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...Salah Amean
 
lecture_mooney.ppt
lecture_mooney.pptlecture_mooney.ppt
lecture_mooney.pptbutest
 
Chatzikonstantinou c ai-se2013_
Chatzikonstantinou c ai-se2013_Chatzikonstantinou c ai-se2013_
Chatzikonstantinou c ai-se2013_caise2013vlc
 
Person re-identification, PhD Day 2011
Person re-identification, PhD Day 2011Person re-identification, PhD Day 2011
Person re-identification, PhD Day 2011Riccardo Satta
 

Similaire à Ch 9-1.Machine Learning: Symbol-based (20)

Ch 9-2.Machine Learning: Symbol-based[new]
Ch 9-2.Machine Learning: Symbol-based[new]Ch 9-2.Machine Learning: Symbol-based[new]
Ch 9-2.Machine Learning: Symbol-based[new]
 
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...Transfer Learning for Improving Model Predictions in Highly Configurable Soft...
Transfer Learning for Improving Model Predictions in Highly Configurable Soft...
 
Part 1
Part 1Part 1
Part 1
 
Introduction
IntroductionIntroduction
Introduction
 
Introduction
IntroductionIntroduction
Introduction
 
Introduction
IntroductionIntroduction
Introduction
 
Machine Learning: Decision Trees Chapter 18.1-18.3
Machine Learning: Decision Trees Chapter 18.1-18.3Machine Learning: Decision Trees Chapter 18.1-18.3
Machine Learning: Decision Trees Chapter 18.1-18.3
 
17- Kernels and Clustering.pptx
17- Kernels and Clustering.pptx17- Kernels and Clustering.pptx
17- Kernels and Clustering.pptx
 
PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...
PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...
PaperReview_ “Few-shot Graph Classification with Contrastive Loss and Meta-cl...
 
Surrogate modeling for industrial design
Surrogate modeling for industrial designSurrogate modeling for industrial design
Surrogate modeling for industrial design
 
Sample prac exam2013
Sample prac exam2013Sample prac exam2013
Sample prac exam2013
 
Artificial Intelligence
Artificial Intelligence Artificial Intelligence
Artificial Intelligence
 
모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로
모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로 모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로
모듈형 패키지를 활용한 나만의 기계학습 모형 만들기 - 회귀나무모형을 중심으로
 
Machine learning (5)
Machine learning (5)Machine learning (5)
Machine learning (5)
 
Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...
Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...
Data Mining Concepts and Techniques, Chapter 10. Cluster Analysis: Basic Conc...
 
.ppt
.ppt.ppt
.ppt
 
lecture_mooney.ppt
lecture_mooney.pptlecture_mooney.ppt
lecture_mooney.ppt
 
Chatzikonstantinou c ai-se2013_
Chatzikonstantinou c ai-se2013_Chatzikonstantinou c ai-se2013_
Chatzikonstantinou c ai-se2013_
 
ANSSummer2015
ANSSummer2015ANSSummer2015
ANSSummer2015
 
Person re-identification, PhD Day 2011
Person re-identification, PhD Day 2011Person re-identification, PhD Day 2011
Person re-identification, PhD Day 2011
 

Plus de butest

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEbutest
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jacksonbutest
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer IIbutest
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.docbutest
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1butest
 
Facebook
Facebook Facebook
Facebook butest
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...butest
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTbutest
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docbutest
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docbutest
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.docbutest
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!butest
 

Plus de butest (20)

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jackson
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer II
 
PPT
PPTPPT
PPT
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.doc
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1
 
Facebook
Facebook Facebook
Facebook
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENT
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.doc
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.doc
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.doc
 
hier
hierhier
hier
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!
 

Ch 9-1.Machine Learning: Symbol-based