SlideShare una empresa de Scribd logo
1 de 27
[object Object],[object Object]
Chapter 10 Contents (1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chapter 10 Contents (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Training  ,[object Object],[object Object],[object Object],[object Object]
Rote Learning  ,[object Object],[object Object],[object Object]
Concept Learning  ,[object Object],[object Object],[object Object],[object Object]
Hypotheses  ,[object Object],[object Object],[object Object],[object Object],[object Object]
Hypotheses - Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
General to Specific Ordering  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Partial Order (sort) ,[object Object],[object Object],[object Object]
More General Hypothesis ,[object Object]
Learning Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Version Spaces  ,[object Object],[object Object],[object Object]
Candidate Elimination ,[object Object],[object Object],[object Object],[object Object]
Inductive Bias ,[object Object],[object Object],[object Object],[object Object]
Decision Tree Induction (1) ,[object Object],[object Object],[object Object]
Decision Tree Induction (2) ,[object Object],[object Object],[object Object],[object Object]
Decision Tree Induction (3) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Values with greatest gain are placed near the top of the tree ,[object Object],[object Object],[object Object],[object Object]
The Problem of Overfitting Black dots represent positive examples, white dots negative. The two lines represent two different hypotheses. In the first diagram, there are just a few items of training data, correctly classified by the hypothesis represented by the darker line. In the second and third diagrams we see the complete set of data, and that the simpler hypothesis which matched the training data less well matches the rest of the data better than the more complex hypothesis, which overfits.
The Nearest Neighbor Algorithm (1) ,[object Object],[object Object],[object Object],[object Object]
The Nearest Neighbor Algorithm (2) ,[object Object],[object Object],[object Object],[object Object]
Neural Networks (1) ,[object Object],[object Object],[object Object],[object Object]
Neural Networks (2) ,[object Object],[object Object],[object Object],[object Object]
Supervised Learning ,[object Object],[object Object],[object Object],[object Object]
Unsupervised Learning ,[object Object],[object Object],[object Object],[object Object]
Reinforcement Learning ,[object Object],[object Object]

Más contenido relacionado

La actualidad más candente

Neural network for machine learning
Neural network for machine learningNeural network for machine learning
Neural network for machine learning
Ujjawal
 
Representing uncertainty in expert systems
Representing uncertainty in expert systemsRepresenting uncertainty in expert systems
Representing uncertainty in expert systems
bhupendra kumar
 
Ppt on artifishail intelligence
Ppt on artifishail intelligencePpt on artifishail intelligence
Ppt on artifishail intelligence
snehal_gongle
 
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
Nimai Chand Das Adhikari
 
SVM Tutorial
SVM TutorialSVM Tutorial
SVM Tutorial
butest
 

La actualidad más candente (19)

Mncs 16-09-4주-변승규-introduction to the machine learning
Mncs 16-09-4주-변승규-introduction to the machine learningMncs 16-09-4주-변승규-introduction to the machine learning
Mncs 16-09-4주-변승규-introduction to the machine learning
 
Chaptr 7 (final)
Chaptr 7 (final)Chaptr 7 (final)
Chaptr 7 (final)
 
15857 cse422 unsupervised-learning
15857 cse422 unsupervised-learning15857 cse422 unsupervised-learning
15857 cse422 unsupervised-learning
 
Ai final module (1)
Ai final module (1)Ai final module (1)
Ai final module (1)
 
Introduction to Applied Machine Learning
Introduction to Applied Machine LearningIntroduction to Applied Machine Learning
Introduction to Applied Machine Learning
 
Deep learning: Mathematical Perspective
Deep learning: Mathematical PerspectiveDeep learning: Mathematical Perspective
Deep learning: Mathematical Perspective
 
Neural network for machine learning
Neural network for machine learningNeural network for machine learning
Neural network for machine learning
 
Perceptron in ANN
Perceptron in ANNPerceptron in ANN
Perceptron in ANN
 
Deep Learning Survey
Deep Learning SurveyDeep Learning Survey
Deep Learning Survey
 
Combining inductive and analytical learning
Combining inductive and analytical learningCombining inductive and analytical learning
Combining inductive and analytical learning
 
Cq4201618622
Cq4201618622Cq4201618622
Cq4201618622
 
Learning
LearningLearning
Learning
 
Representing uncertainty in expert systems
Representing uncertainty in expert systemsRepresenting uncertainty in expert systems
Representing uncertainty in expert systems
 
Ppt on artifishail intelligence
Ppt on artifishail intelligencePpt on artifishail intelligence
Ppt on artifishail intelligence
 
Introduction to deep learning
Introduction to deep learningIntroduction to deep learning
Introduction to deep learning
 
Decision tree learning
Decision tree learningDecision tree learning
Decision tree learning
 
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
TFFN: Two Hidden Layer Feed Forward Network using the randomness of Extreme L...
 
Neural Networks by Priyanka Kasture
Neural Networks by Priyanka KastureNeural Networks by Priyanka Kasture
Neural Networks by Priyanka Kasture
 
SVM Tutorial
SVM TutorialSVM Tutorial
SVM Tutorial
 

Destacado

MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...
MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...
MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...
butest
 
Return of the Imitation Game: 1. Commercial Requirements and ...
Return of the Imitation Game: 1. Commercial Requirements and ...Return of the Imitation Game: 1. Commercial Requirements and ...
Return of the Imitation Game: 1. Commercial Requirements and ...
butest
 
Monoton-working version-1995.doc
Monoton-working version-1995.docMonoton-working version-1995.doc
Monoton-working version-1995.doc
butest
 
see CV
see CVsee CV
see CV
butest
 
Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...
Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...
Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...
butest
 
EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
butest
 

Destacado (7)

MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...
MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...
MEDSHOW 2010 – MEDS13 CLASS ACT – “THE INTERN” Lights down. Static ...
 
Return of the Imitation Game: 1. Commercial Requirements and ...
Return of the Imitation Game: 1. Commercial Requirements and ...Return of the Imitation Game: 1. Commercial Requirements and ...
Return of the Imitation Game: 1. Commercial Requirements and ...
 
[ppt]
[ppt][ppt]
[ppt]
 
Monoton-working version-1995.doc
Monoton-working version-1995.docMonoton-working version-1995.doc
Monoton-working version-1995.doc
 
see CV
see CVsee CV
see CV
 
Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...
Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...
Spis treści 1 Wstęp 4 2 Przegląd literatury 6 3 Projekt aplikacji ...
 
EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
 

Similar a coppin chapter 10e.ppt

Cognitive Science Unit 4
Cognitive Science Unit 4Cognitive Science Unit 4
Cognitive Science Unit 4
CSITSansar
 
Sample_Subjective_Questions_Answers (1).pdf
Sample_Subjective_Questions_Answers (1).pdfSample_Subjective_Questions_Answers (1).pdf
Sample_Subjective_Questions_Answers (1).pdf
AaryanArora10
 
Soft Computing-173101
Soft Computing-173101Soft Computing-173101
Soft Computing-173101
AMIT KUMAR
 
LearningAG.ppt
LearningAG.pptLearningAG.ppt
LearningAG.ppt
butest
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
butest
 
Introduction to Machine Learning Aristotelis Tsirigos
Introduction to Machine Learning Aristotelis Tsirigos Introduction to Machine Learning Aristotelis Tsirigos
Introduction to Machine Learning Aristotelis Tsirigos
butest
 

Similar a coppin chapter 10e.ppt (20)

Classifiers
ClassifiersClassifiers
Classifiers
 
Artificial Neural Networks for NIU
Artificial Neural Networks for NIUArtificial Neural Networks for NIU
Artificial Neural Networks for NIU
 
Cognitive Science Unit 4
Cognitive Science Unit 4Cognitive Science Unit 4
Cognitive Science Unit 4
 
B42010712
B42010712B42010712
B42010712
 
Deep learning MindMap
Deep learning MindMapDeep learning MindMap
Deep learning MindMap
 
Survey on Artificial Neural Network Learning Technique Algorithms
Survey on Artificial Neural Network Learning Technique AlgorithmsSurvey on Artificial Neural Network Learning Technique Algorithms
Survey on Artificial Neural Network Learning Technique Algorithms
 
PREDICT 422 - Module 1.pptx
PREDICT 422 - Module 1.pptxPREDICT 422 - Module 1.pptx
PREDICT 422 - Module 1.pptx
 
Sample_Subjective_Questions_Answers (1).pdf
Sample_Subjective_Questions_Answers (1).pdfSample_Subjective_Questions_Answers (1).pdf
Sample_Subjective_Questions_Answers (1).pdf
 
Nueral fuzzy system.pptx
Nueral fuzzy system.pptxNueral fuzzy system.pptx
Nueral fuzzy system.pptx
 
Soft Computing-173101
Soft Computing-173101Soft Computing-173101
Soft Computing-173101
 
LearningAG.ppt
LearningAG.pptLearningAG.ppt
LearningAG.ppt
 
Methodological study of opinion mining and sentiment analysis techniques
Methodological study of opinion mining and sentiment analysis techniquesMethodological study of opinion mining and sentiment analysis techniques
Methodological study of opinion mining and sentiment analysis techniques
 
Artifical Neural Network and its applications
Artifical Neural Network and its applicationsArtifical Neural Network and its applications
Artifical Neural Network and its applications
 
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques  Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
 
Machine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.pptMachine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.ppt
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
 
Introduction to Machine Learning Aristotelis Tsirigos
Introduction to Machine Learning Aristotelis Tsirigos Introduction to Machine Learning Aristotelis Tsirigos
Introduction to Machine Learning Aristotelis Tsirigos
 
nncollovcapaldo2013-131220052427-phpapp01.pdf
nncollovcapaldo2013-131220052427-phpapp01.pdfnncollovcapaldo2013-131220052427-phpapp01.pdf
nncollovcapaldo2013-131220052427-phpapp01.pdf
 
nncollovcapaldo2013-131220052427-phpapp01.pdf
nncollovcapaldo2013-131220052427-phpapp01.pdfnncollovcapaldo2013-131220052427-phpapp01.pdf
nncollovcapaldo2013-131220052427-phpapp01.pdf
 
Introduction Of Artificial neural network
Introduction Of Artificial neural networkIntroduction Of Artificial neural network
Introduction Of Artificial neural network
 

Más de butest

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 TRIAL
butest
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jackson
butest
 
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 TRIAL
butest
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer II
butest
 
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
butest
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.doc
butest
 
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
butest
 
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 ANNOUNCEMENT
butest
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.doc
butest
 
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
butest
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.doc
butest
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!
butest
 
Download
DownloadDownload
Download
butest
 

Más de butest (20)

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!
 
Download
DownloadDownload
Download
 

coppin chapter 10e.ppt

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. The Problem of Overfitting Black dots represent positive examples, white dots negative. The two lines represent two different hypotheses. In the first diagram, there are just a few items of training data, correctly classified by the hypothesis represented by the darker line. In the second and third diagrams we see the complete set of data, and that the simpler hypothesis which matched the training data less well matches the rest of the data better than the more complex hypothesis, which overfits.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.