lec01-conceptLearning

lec01-conceptLearning - Concept Learning Inducing general...

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CS464 Introduction to Machine Learning 1 Concept Learning Inducing general functions from specific training examples is a main issue of machine learning. Concept Learning: Acquiring the definition of a general category from given sample positive and negative training examples of the category. Concept Learning can seen as a problem of searching through a predefined space of potential hypotheses for the hypothesis that best fits the training examples. The hypothesis space has a general-to-specific ordering of hypotheses, and the search can be efficiently organized by taking advantage of a naturally occurring structure over the hypothesis space.
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CS464 Introduction to Machine Learning 2 Concept Learning A Formal Definition for Concept Learning: Inferring a boolean-valued function from training examples of its input and output. An example for concept-learning is the learning of bird-concept from the given examples of birds (positive examples) and non-birds (negative examples). We are trying to learn the definition of a concept from given examples.
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CS464 Introduction to Machine Learning 3 A Concept Learning Task – Enjoy Sport Training Examples Example Sky AirTemp Humidity Wind Water Forecast EnjoySport 1 Sunny Warm Normal Strong Warm Same YES 2 Sunny Warm High Strong Warm Same YES 3 Rainy Cold High Strong Warm Change NO 4 Sunny Warm High Strong Warm Change YES A set of example days, and each is described by six attributes. The task is to learn to predict the value of EnjoySport for arbitrary day, based on the values of its attribute values. ATTRIBUTES CONCEPT
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CS464 Introduction to Machine Learning 4 EnjoySport – Hypothesis Representation Each hypothesis consists of a conjuction of constraints on the instance attributes . Each hypothesis will be a vector of six constraints, specifying the values of the six attributes (Sky, AirTemp, Humidity, Wind, Water, and Forecast). Each attribute will be: ? - indicating any value is acceptable for the attribute ( don’t care ) single value – specifying a single required value (ex. Warm) ( specific ) 0 - indicating no value is acceptable for the attribute ( no value )
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CS464 Introduction to Machine Learning 5 Hypothesis Representation A hypothesis: Sky AirTemp Humidity Wind Water Forecast < Sunny, ? , ? , Strong , ? , Same > The most general hypothesis – that every day is a positive example <?, ?, ?, ?, ?, ?> The most specific hypothesis – that no day is a positive example <0, 0, 0, 0, 0, 0> EnjoySport concept learning task requires learning the sets of days for which EnjoySport=yes, describing this set by a conjunction of constraints over the instance attributes.
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CS464 Introduction to Machine Learning 6 EnjoySport Concept Learning Task Given Instances X : set of all possible days, each described by the attributes Sky – (values: Sunny, Cloudy, Rainy) AirTemp – (values: Warm, Cold) Humidity – (values: Normal, High) Wind – (values: Strong, Weak)
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This note was uploaded on 12/27/2009 for the course CS 464 taught by Professor Demir during the Fall '08 term at Bilkent University.

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lec01-conceptLearning - Concept Learning Inducing general...

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