'Training examples' presentation slideshows

CptS 440 / 540 Artificial Intelligence

CptS 440 / 540 Artificial Intelligence. Machine Learning. Machine Learning. What is learning?. Definitions. Webster

1.23k views • 110 slides

CS 1674: Intro to Computer Vision Visual Recognition

CS 1674: Intro to Computer Vision Visual Recognition. Prof. Adriana Kovashka University of Pittsburgh February 27, 2018. Plan for today. What is recognition? a.k.a. classification, categorization Support vector machines Separable case / non-separable case

1.02k views • 86 slides

Support Vector Machines

1.22k views • 83 slides

Bias-Variance Analysis of Ensemble Learning

Bias-Variance Analysis of Ensemble Learning. Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331 http://www.cs.orst.edu/~tgd. Outline. Bias-Variance Decomposition for Regression Bias-Variance Decomposition for Classification

1.23k views • 71 slides

Tomasz Malisiewicz tomasz@cmu Advanced Machine Perception February 2006

A Bayesian Hierarchical Model for Learning Natural Scene Categories L. Fei-Fei and P. Perona. CVPR 2005 Discovering objects and their location in images J. Sivic, B. Russell, A. Efros, A. Zisserman and B. Freeman. ICCV 2005. Tomasz Malisiewicz tomasz@cmu.edu Advanced Machine Perception

779 views • 59 slides

Decision Trees in the Big Picture

Decision Trees in the Big Picture. Classification (vs. Rule Pattern Discovery) Supervised Learning (vs. Unsupervised) Inductive Generation (vs. Discrimination). Example . Class-labels. Example . Example. Inner nodes are ATTRIBUTES. a ge. yes. y outh. s enior. m iddle_aged. no.

701 views • 57 slides

Data Mining in Artificial Intelligence: Decision Trees

Data Mining in Artificial Intelligence: Decision Trees. Outline. Introduction: Data Flood Top-Down Decision Tree Construction Choosing the Splitting Attribute Information Gain and Gain Ratio Pruning. Trends leading to Data Flood. More data is generated:

839 views • 56 slides

CS364 Artificial Intelligence Machine Learning

CS364 Artificial Intelligence Machine Learning. Matthew Casey. Learning Outcomes. Describe methods for acquiring human knowledge Through experience Evaluate which of the acquisition methods would be most appropriate in a given situation Limited data available through example.

807 views • 48 slides

Decision Tree

Decision Tree. By Wang Rui State Key Lab of CAD&CG 2004-03-17. Review. Concept learning Induce Boolean function from a sample of positive/negative training examples. Concept learning can be cast as Searching through predefined hypotheses space Searching Algorithm: FIND-S

998 views • 44 slides

LEARNING

611 views • 43 slides

CS 343: Artificial Intelligence Machine Learning

530 views • 35 slides

Learning set of rules

Learning set of rules. Content. Introduction Sequential Covering Algorithms Learning Rule Sets: Summary Learning First-Order Rule Learning Sets of First-Order Rules: FOIL Summary. Introduction. If-Then Rules Are very expressive Easy to understand Rules with variables: Horn-clauses

710 views • 35 slides

Machine Learning Chapter 3. Decision Tree Learning

Machine Learning Chapter 3. Decision Tree Learning. Tom M. Mitchell. Abstract. Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting. Decision Tree for PlayTennis. A Tree to Predict C-Section Risk. Learned from medical records of 1000 women

787 views • 29 slides

Deformable Models (Active Shape Models for Facial Analysis)

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Text Categorization With Support Vector Machines: Learning With Many Relevant Features

Text Categorization With Support Vector Machines: Learning With Many Relevant Features. By Thornsten Joachims Presented By Meghneel Gore. Goal of Text Categorization. Classify documents into a number of pre-defined categories. Documents can be in multiple categories

539 views • 24 slides

Kunstmatige Intelligentie / RuG

370 views • 23 slides

Input Space versus Feature Space in Kernel-Based Methods

Input Space versus Feature Space in Kernel-Based Methods. Scholkopf, Mika, Burges, Knirsch, Muller, Ratsch, Smola presented by: Joe Drish Department of Computer Science and Engineering University of California, San Diego. Goals. Objectives of the paper.

549 views • 23 slides

CSC 9010: Text Mining Applications

CSC 9010: Text Mining Applications. Dr. Paula Matuszek Paula_A_Matuszek@glaxosmithkline.com (610) 270-6851. So What Next?. Evaluating systems Systems available Some good resources. Evaluating Text Mining Systems. There are dozens of text mining tools and systems available commercial

403 views • 19 slides

Machine Learning: Lecture 3

Machine Learning: Lecture 3. Decision Tree Learning (Based on Chapter 3 of Mitchell T.., Machine Learning, 1997). Decision Tree Representation. Outlook. Sunny. Rain. Overcast. Humidity Wind. Yes. High. Weak. Normal. Strong. Yes. No. Yes. No.

434 views • 13 slides

Lazy vs. Eager Learning

Lazy vs. Eager Learning. Lazy vs. eager learning Lazy learning (e.g., instance-based learning): Simply stores training data (or only minor processing) and waits until it is given a test tuple

778 views • 6 slides