Machine Learning

Chapter 5 Machine Learning

Learning 1. Rote learning rote(โรท) n. วิถีทาง,ทางเดิน,วิธีการตามปกติ, (by rote จากความทรงจำ),การท่องจำอย่างเดียว S. repetition 2. Learning by taking advice 3. Learning by problem solving Parameter adjustment Macro-Operators 4. Learning from examples Induction : Winston’s learning program p.458 Version Spaces : Candidate eliminate algorithm Decision tree 5. Explanation-based learning p 482 6. Formal learning theory Chapter 5

Winston’s learning program Chapter 5

Winston’s learning program Concept : P.459 Begin with a structural description of one known instance of the concept. Call the description the concept definition. Examine descriptions of other known instances of the concepts. Generalize the definitionto include them. Examine descriptions of near misses of concept, Restrict the definitionto exclude these. Chapter 5

HOUSE OF 17.2 ARCH OF 17.2 ARCH OF 17.2 Chapter 5

Winston’s learning program Chapter 5

Winston’s learning program p.458 Block world concept : Figure 17.2 p. 459 Structure description : Figure 17.3 p. 460 The comparison of two arches : Figure 17.4 p. 461 The arch description after two examples : Figure 17.5 p. 462 The arch “description after a near miss : Figure 17.6 p. 463 use semantic networks to describe block structures use matching process to detect similarities and differences between structures use isa hierarchy to describe relationship among already known objects Chapter 5

Semantic Network isa isa Chapter 5

Semantic Network Chapter 5

Version Spaces The goal : to produce a description that is consistent with all positive examples but no negative examples in the training set. use frame representing concept for car see Figure 17.7 p. 463 Features/Slots : { value1, value2,...,valueN } origin : { Japan, USA, Britain } Variables : X1, X2, X3 concept space : see Figure 17.11 Concept of Version Spaces p. 466 variables target concept all training instance Chapter 5

Version Spaces Chapter 5

Version Spaces • version space = current hypothesis = subset of concept space = largest collection of descriptions that is consistent with all the training examples seen so far. • concept space = G or S • G = contain the most general descriptions consistent with the training example seen so far. • S = contain the most specific descriptions consistent with training examples • positive example (+)  moveS to more specific • negative example (-)  move Gto more specific • if G and S sets converge  the hypothesis is a single concept description Chapter 5

Version Spaces • Candidate Eliminate Algorithm p.466-467 •  algorithm that use to narrow down the version space •  by remove any descriptions that are inconsistent with set G and set S • Car Example Figure 17.7 Concept Car : p. 463 Figure 17.8 Representation language for car : p. 464 Figure 17.9 The concept Japanese car : p. 464 Figure 17.10 Partial ordering of concepts : p. 465 Figure 17.12 Positive and negative examples of car : p. 467 Chapter 5

Chapter 5

Candidate Eliminate Algorithm Chapter 5

Version Spaces We want “Japanese economy car” From Figure 17.12 Positive and negative examples of car : p. 467 [origin = X1, manufacture = X2, color = X3, decade = X4, type = X5] GET EX1 (+) G = {(X1, X2, X3, X4, X5)} S = {(Japan,Honda, Blue,1980,Economy}) =Figure 17.12 in EX1 GET EX2 (-) G = {(X1, Honda, X3, X4, X5), (X1, X2, Blue, X4, X5) , (X1, X2, X3, 1980, X5), (X1, X2, X3, X4, Economy)} S = {(Japan,Honda, Blue,1980,Economy}) ** the same because (-) example GET EX3 (+) check G first, G = {(X1, X2, Blue, X4, X5) ,(X1, X2, X3, X4, Economy)} S = {(Japan,X2, Blue,X4,Economy}) GET EX4 (-) check G first, G = {(Japan, X2, Blue, X4, X5) , (Japan, X2, X3, X4, Economy)} S = {(Japan,X2, Blue,X4,Economy}) ** the same because (-) example GET EX5 (+) check G first, G = {(Japan, X2, X3, X4, Economy)} S = {(Japan,X2, X3,X4, Economy}) Chapter 5

Version Spaces • Note : The algorithm is least commitment algorithm : produce as little as possible at each step • Problems 1.) S and G may not converge to a single hypothesis 2. ) if there is a noise (inconsistent data)  the algorithm will be premature, we may prune the target concept too fast * For example if the data number three given the negative sign (-) instance of positive sign (+) ... no matter how much the data is we can not find the concept.... * How to fix this problem is to maintain several G and S sets BUT it is costly and may have the bounded inconsistency problem 3.) We can not use OR in the questions ask * For example : Italian sport car or German luxury car” Chapter 5

Decision Tree ID3 Program = to classify a particular input, we start at the top of the tree and answer questions until we reach a leaf, where the classification is stored. See Figure 17.13 Decision tree p. 470 1. Choose window = random subset of training examples to train 2. Outside window = use to test the decision tree 3. Use empirical evidence (iterative method) to build up decision tree 4. Building a node = choosing some attribute to divide training instance into subset consider (+) sign Can use with OR .... just change (-) sign into (+) sign Problems : noisy input, attribute value may be unknown, may have large decision tree and hard to understand relationship See Figure 17.13 Chapter 5

Decision Tree Chapter 5

Explanation-Based Learning • provide explanation • depend on domain theory/ domain knowledge Chapter 5

Formal Learning Theory • Given positive and negative examples • produce algorithm that will classify future examples correctly with probability 1/h • Complexity of learning : • the error tolerance (h) • the number of binary features present in the examples (t) • the size of the rule necessary to make the discrimination (f) Chapter 5

Formal Learning Theory • if the number of training examples required is polynomial in h,t, and f  then the concept is learnable. • few training examples are needed  learnable • we restrict the learner to the positive examples only. • See Figure 12.22 Concept of elephant P. 483 • elephant = “gray, mammal, large” Chapter 5

Formal Learning Theory Chapter 5

2 • emphasis to all BEANS : all instances Induction • induction : A method of reasoning by which one infers a generalization from a series of instances. • Inductive syllogisms are of the following form: 1. These beans are from this bag. (and these beans..., and these beans..., etc.) 2. These beans are (all) white. # 3 Therefore, all beans from this bag are white. • In a much broader sense, induction can be thought to include various other forms of reasoning including reasoning, inference to cause form symptoms, and confirmation of laws and theories. • 1 Chapter 5

2 • emphasis to one BEAN : one instance Deduction • deduction - A method of reasoning by which one infers a conclusion from a set of sentences by employing the axioms and rules of inference for agiven logical system. • Use the term 'deduction' in a general sense to denote the fact that a conclusion follows necessarily from the premises. • Deductive syllogisms in quantificational predicate calculus are of the following form: 1. All beans from this bag are white.... 2. These beans are from this bag. #4 Therefore, these beans are white..... • 1 Chapter 5

emphasis to one BEANS Abduction • abduction -A method of reasoning by which one infers to the ......best explanation..... • - A heuristic procedure that reasons inductively from available empirical evidence to the discovery of the probable hypotheses that would best explain its occurrence. • Abductive syllogisms are of the following form: #3 All beans from this bag are white #4 These beans are white. Chapter 5

The End Chapter 5

Machine Learning