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CS 391L: Machine Learning: Computational Learning Theory PowerPoint PPT Presentation

CS 391L: Machine Learning: Computational Learning Theory - CS 391L: Machine Learning: Computational Learning Theory Raymond J. Mooney University of Texas at Austin

CS 391L: Machine Learning: Computational Learning Theory Raymond J. Mooney University of Texas at Austin

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CS 391L: Machine Learning: Decision Tree Learning PowerPoint PPT Presentation

CS 391L: Machine Learning: Decision Tree Learning - CS 391L: Machine Learning: Decision Tree Learning Raymond J. Mooney University of Texas at Austin

CS 391L: Machine Learning: Decision Tree Learning Raymond J. Mooney University of Texas at Austin

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CS 391L: Machine Learning: Rule Learning PowerPoint PPT Presentation

CS 391L: Machine Learning: Rule Learning - ... to-specific (top-down) (CN2, FOIL) Specific-to-general (bottom-up) ... Rule in FOIL ... Foil Gain Metric. Want to achieve two goals. Decrease coverage of ...

... to-specific (top-down) (CN2, FOIL) Specific-to-general (bottom-up) ... Rule in FOIL ... Foil Gain Metric. Want to achieve two goals. Decrease coverage of ...

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CS 391L: Machine Learning Text Categorization PowerPoint PPT Presentation

CS 391L: Machine Learning Text Categorization - 1. CS 391L: Machine Learning. Text Categorization. Raymond J. Mooney. University of Texas at Austin ... lottery. win. Friday. exam. computer. May. PM. test ...

1. CS 391L: Machine Learning. Text Categorization. Raymond J. Mooney. University of Texas at Austin ... lottery. win. Friday. exam. computer. May. PM. test ...

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CS 391L: Machine Learning Neural Networks PowerPoint PPT Presentation

CS 391L: Machine Learning Neural Networks - ... complex functions, like sine waves in Fourier analysis. ... Handwriting recognition. 39. Issues in Neural Nets. More efficient training methods: Quickprop ...

... complex functions, like sine waves in Fourier analysis. ... Handwriting recognition. 39. Issues in Neural Nets. More efficient training methods: Quickprop ...

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CS 391L: Machine Learning: Bayesian Learning: Na PowerPoint PPT Presentation

CS 391L: Machine Learning: Bayesian Learning: Na - All probabilities between 0 and 1. True proposition has ... P(true) = 1 P(false) = 0. The probability of disjunction is: A. B. 3. Conditional Probability ...

All probabilities between 0 and 1. True proposition has ... P(true) = 1 P(false) = 0. The probability of disjunction is: A. B. 3. Conditional Probability ...

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CS 391L: Machine Learning Neural Networks - Synapses change size and strength with experience. ... Assume supervised training examples giving the desired output for a unit given a ...

Synapses change size and strength with experience. ... Assume supervised training examples giving the desired output for a unit given a ...

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CS 391L: Machine Learning Natural Language Learning PowerPoint PPT Presentation

CS 391L: Machine Learning Natural Language Learning - Many jokes rely on the ... instance of 'line' labeled with one of 6 senses ... people fighting for a place in line have no trouble filling in the blanks. ...

Many jokes rely on the ... instance of 'line' labeled with one of 6 senses ... people fighting for a place in line have no trouble filling in the blanks. ...

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CS 391L: Machine Learning: Computational Learning Theory - A hypothesis space is said to shatter a set of instances iff for every partition ... No five instances can be shattered since there can be at most 4 distinct extreme ...

A hypothesis space is said to shatter a set of instances iff for every partition ... No five instances can be shattered since there can be at most 4 distinct extreme ...

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CS 391L: Machine Learning: Instance Based Learning PowerPoint PPT Presentation

CS 391L: Machine Learning: Instance Based Learning - Unlike other learning algorithms, does not involve construction of an explicit ... To compensate for difference in units across features, scale all continuous ...

Unlike other learning algorithms, does not involve construction of an explicit ... To compensate for difference in units across features, scale all continuous ...

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CS 391L: Machine Learning: Rule Learning - If-then rules in logic are a standard representation of knowledge that have ... Rules are post-pruned by greedily removing antecedents or rules until ...

If-then rules in logic are a standard representation of knowledge that have ... Rules are post-pruned by greedily removing antecedents or rules until ...

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CS 391L: Machine Learning: Bayesian Learning: Beyond Na PowerPoint PPT Presentation

CS 391L: Machine Learning: Bayesian Learning: Beyond Na - Equivalent to a one-layer backpropagation neural net. Logistic regression is the source of the sigmoid function used in backpropagation. ...

Equivalent to a one-layer backpropagation neural net. Logistic regression is the source of the sigmoid function used in backpropagation. ...

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CS 391L: Machine Learning Clustering PowerPoint PPT Presentation

CS 391L: Machine Learning Clustering - Partition unlabeled examples into disjoint subsets of clusters, such that: ... mammal worm insect crustacean. invertebrate. 5. Aglommerative vs. Divisive Clustering ...

Partition unlabeled examples into disjoint subsets of clusters, such that: ... mammal worm insect crustacean. invertebrate. 5. Aglommerative vs. Divisive Clustering ...

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CS 391L: Machine Learning: Decision Tree Learning - Nodes test features, there is one branch for each value of the feature, and ... Performs hill-climbing (greedy search) that may only find a locally-optimal solution. ...

Nodes test features, there is one branch for each value of the feature, and ... Performs hill-climbing (greedy search) that may only find a locally-optimal solution. ...

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CS 391L: Machine Learning Neural Networks - CS 391L: Machine Learning Neural Networks Raymond J. Mooney University of Texas at Austin Neural Networks Analogy to biological neural systems, the most robust ...

CS 391L: Machine Learning Neural Networks Raymond J. Mooney University of Texas at Austin Neural Networks Analogy to biological neural systems, the most robust ...

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CS 391L: Machine Learning: Ensembles PowerPoint PPT Presentation

CS 391L: Machine Learning: Ensembles - ... Boosting provides a larger increase in accuracy than Bagging. ... Bagging more consistently provides a modest improvement. ... Combining Boosting and Bagging. ...

... Boosting provides a larger increase in accuracy than Bagging. ... Bagging more consistently provides a modest improvement. ... Combining Boosting and Bagging. ...

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CS 391L: Machine Learning: Bayesian Learning: Na - If Y and all Xi and binary, this requires specifying only 2n parameters: ... During testing, estimate P(Xi | Y=yk) for a given example, using the Gaussian ...

If Y and all Xi and binary, this requires specifying only 2n parameters: ... During testing, estimate P(Xi | Y=yk) for a given example, using the Gaussian ...

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CS 391L: Machine Learning: Computational Learning Theory - University of Texas at Austin. 2. Learning Theory ... If there exists at least one subset of X of size d that can be shattered then VC ...

University of Texas at Austin. 2. Learning Theory ... If there exists at least one subset of X of size d that can be shattered then VC ...

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CS 391L: Machine Learning Introduction PowerPoint PPT Presentation

CS 391L: Machine Learning Introduction - CS 391L: Machine Learning Introduction Raymond J. Mooney University of Texas at Austin What is Learning? Herbert Simon: Learning is any process by which a system ...

CS 391L: Machine Learning Introduction Raymond J. Mooney University of Texas at Austin What is Learning? Herbert Simon: Learning is any process by which a system ...

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CS%20391L:%20Machine%20Learning:%20Inductive%20Classification PowerPoint PPT Presentation

CS%20391L:%20Machine%20Learning:%20Inductive%20Classification - ... binary function C={0,1} ({true,false}, {positive, negative}) then it is called a ... An instance, x X, is said to satisfy an hypothesis, h, iff h(x)=1 (positive) ...

... binary function C={0,1} ({true,false}, {positive, negative}) then it is called a ... An instance, x X, is said to satisfy an hypothesis, h, iff h(x)=1 (positive) ...

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CS 60050 Machine Learning PowerPoint PPT Presentation

CS 60050 Machine Learning - Permanently prune the node that results in the greatest increase in accuracy on ... the pruned tree learned in each trial. Let C be the average pruned-tree ...

Permanently prune the node that results in the greatest increase in accuracy on ... the pruned tree learned in each trial. Let C be the average pruned-tree ...

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CS 391L: Machine Learning: Experimental Evaluation PowerPoint PPT Presentation

CS 391L: Machine Learning: Experimental Evaluation - Comparing Two ... Only compares two specific hypotheses regardless of the methods used to ... Comparing the average accuracy of hypotheses produced by two ...

Comparing Two ... Only compares two specific hypotheses regardless of the methods used to ... Comparing the average accuracy of hypotheses produced by two ...

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