Machine Learning

  VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM   •         The key idea in the CANDIDATE-ELIMINATION algorithm is to output •         a description of the set of all hypotheses consistent with the training examples Note difference between definitions of consistent and satisfies ·        An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive or negative example of the target concept . ·        An example x is said to consistent with hypothesis h iff h(x) = c(x) The LIST-THEN-ELIMINATION algorithm The LIST-THEN-ELIMINATE algorithm first initializes the version space to contain all hypotheses in H and then eliminates any hypothesis found inconsistent with any training example . ___________________________________________________________________________ 1. VersionSpace c a list containing every hypothesis in H 2. For each training example , (x, c(x)) remove from VersionSpace any hypothesis h for which h

  A CONCEPT LEARNING TASK •         Consider the example task of learning the target concept " Days on which Tom enjoys his favourite water sport ” Table : Positive and negative training examples for the target concept EnjoySport .   ü   The task is to learn to predict the value of EnjoySport for an arbitrary day , based on the values of its other attributes? ü   What hypothesis representation is provided to the learner?   ·        Let’s consider a simple representation in which each hypothesis consists of a conjunction of constraints on the instance attributes . ·        Let each hypothesis be a vector of six constraints , specifying the values of the six attributes ·        Sky ·        AirTemp ·        Humidity ·        Wind ·        Water ·        Forecast •         For each attribute , the hypothesis will either ü   Indicate by a "?' that any value is acceptable for this attribute , ü   Specify a single required value (e.g., Warm ) for the attribute , or ü   I