Titlebook: Innovations in Machine Learning; Theory and Applicati Dawn E. Holmes,Lakhmi C. Jain Book 2006 Springer-Verlag Berlin Heidelberg 2006 Case-b

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Support Vector Inductive Logic Programming,ound knowledge, though the final combining function for the ILP-learned clauses is an SVM rather than a logical conjunction. We evaluate SVILP empirically against related approaches, including an industry-standard toxin predictor called TOPKAT. Evaluation is conducted on a new broad-ranging toxicity

BACLE

Ferritin

The Structure of Version Space,r case of linear classifiers we show that classifiers found by the classical p erceptron algorithm have guarantees bounded by the size of version space. These results are complemented with an empirical study for kernel classifiers on the task of handwritten digit recognition which demonstrates that

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A Bayesian Approach to Causal Discovery,usal Markov condition, but the two differ significantly in theory and practice. An important difference between the approaches is that the constraint-based approach uses categorical information about conditional-independence constraints in the domain, whereas the Bayesian approach weighs the degree

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花束

Jargon

N-1 Experiments Suffice to Determine the Causal Relations Among N Variables, N - 1 experiments suffice to determine the causal relations among N>2 variables when each experiment randomizes at most one variable. We show the same bound holds for adaptive learners, but does not hold for N > 4 when each experiment can simultaneously randomize more than one variable. This bound

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antenna

Neural Probabilistic Language Models,difficult because of the .: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the tr

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