# Lesson 34 - Module 12 Machine Learning Version 1 CSE IIT,...

This preview shows pages 1–6. Sign up to view the full content.

Module 12 Machine Learning Version 1 CSE IIT, Kharagpur

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Lesson 34 Learning From Observations Version 1 CSE IIT, Kharagpur
12.2 Concept Learning Definition: The problem is to learn a function mapping examples into two classes: positive and negative. We are given a database of examples already classified as positive or negative. Concept learning: the process of inducing a function mapping input examples into a Boolean output. Examples: ± Classifying objects in astronomical images as stars or galaxies ± Classifying animals as vertebrates or invertebrates Example: Classifying Mushrooms Class of Tasks: Predicting poisonous mushrooms Performance: Accuracy of classification Experience: Database describing mushrooms with their class Knowledge to learn: Function mapping mushrooms to {0,1} where 0:not-poisonous and 1:poisonous Representation of target knowledge: conjunction of attribute values. Learning mechanism: candidate-elimination Representation of instances: Features: color {red, brown, gray} size {small, large} shape {round,elongated} land {humid,dry} air humidity {low,high} texture {smooth, rough} Input and Output Spaces: X : The space of all possible examples (input space). Y: The space of classes (output space). An example in X is a feature vector X. For instance: X = (red,small,elongated,humid,low,rough) X is the cross product of all feature values. Only a small subset of instances is available in the database of examples. Version 1 CSE IIT, Kharagpur

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
X X Y Y = = { { 0 0 , , 1 1 } } Training Examples: D : The set of training examples. D is a set of pairs { (x,c(x)) }, where c is the target concept. c is a subset of the universe of discourse or the set of all possible instances. Example of D: ((red,small,round,humid,low,smooth), poisonous) ((red,small,elongated,humid,low,smooth), poisonous) ((gray,large,elongated,humid,low,rough), not-poisonous) ((red,small,elongated,humid,high,rough), poisonous) Hypothesis Representation Any hypothesis h is a function from X to Y h: X Æ Y We will explore the space of conjunctions. Special symbols: ¾ ? Any value is acceptable ¾ 0 no value is acceptable Consider the following hypotheses: (?,?,?,?,?,?): all mushrooms are poisonous (0,0,0,0,0,0): no mushroom is poisonous Version 1 CSE IIT, Kharagpur
Hypotheses Space: The space of all hypotheses is represented by H

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## Lesson 34 - Module 12 Machine Learning Version 1 CSE IIT,...

This preview shows document pages 1 - 6. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online