Algorithmic Learning Theory: 15th International Conference, by Ayumi Shinohara (auth.), Shoham Ben-David, John Case, Akira

By Ayumi Shinohara (auth.), Shoham Ben-David, John Case, Akira Maruoka (eds.)

Algorithmic studying concept is arithmetic approximately computing device courses which examine from event. This comprises huge interplay among quite a few mathematical disciplines together with concept of computation, information, and c- binatorics. there's additionally significant interplay with the sensible, empirical ?elds of computer and statistical studying within which a vital objective is to foretell, from prior info approximately phenomena, necessary beneficial properties of destiny facts from an identical phenomena. The papers during this quantity conceal a extensive diversity of issues of present examine within the ?eld of algorithmic studying idea. we've got divided the 29 technical, contributed papers during this quantity into 8 different types (corresponding to 8 classes) re?ecting this extensive diversity. the types featured are Inductive Inf- ence, Approximate Optimization Algorithms, on-line series Prediction, S- tistical research of Unlabeled info, PAC studying & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. lower than we supply a short assessment of the ?eld, putting each one of those subject matters within the normal context of the ?eld. Formal versions of automatic studying re?ect a variety of elements of the wide variety of actions that may be seen as studying. A ?rst dichotomy is among viewing studying as an inde?nite strategy and viewing it as a ?nite task with a de?ned termination. Inductive Inference types specialize in inde?nite studying strategies, requiring in simple terms eventual good fortune of the learner to converge to a passable conclusion.

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Extra info for Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings

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35] L. Ngo and P. Haddawy. Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171(1–2):147–177, 1997. [36] S. Nienhuys-Cheng and R. de Wolf. Foundations of Inductive Logic Programming. Springer-Verlag, 1997. [37] J. Pearl. Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 2. edition, 1991. [38] A. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University, 2000. [39] G. Plotkin. A note on inductive generalization.

Under the presence of background knowledge, the hypothesis H together with the background theory B should cover all positive and none of the negative examples. The ability to provide declarative background knowledge to the learning engine is viewed as one of the strengths of inductive logic programming. 1 For the learning from interpretations setting, we slightly deviate from the standard definition in the literature for didactic purposes. 1 21 Learning from Entailment Learning from entailment is by far the most popular inductive logic programming systems and it is addressed by a wide variety of well-known inductive logic programming systems such as FOIL [41], PROGOL [30], and ALEPH [44].

1. Behaviour of the randomized second-order Perceptron for on-line learning on a newsstory categorization task based on the Reuters Corpus Volume 1. For each value of the parameter C a pair of curves is plotted as a function of the number of instances observed. The increasing curve is the average F-measure (a kind of accuracy) and the decreasing curve is the rate of labels queried. Note that, for the range of parameter values displayed, the performance does not get significantly worse as the parameter value decreases causing the label rate to drop faster.

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