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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.Abstract:
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata by experimentation appendix - some tools for probabilistic analysis.read more
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A bayesian approach to temporal data clustering using the hidden markov model methodology
Cen Li,Gautam Biswas +1 more
TL;DR: This dissertation develops a complete algorithm for temporal data clustering using the HMM methodology, where partition structure and HMM model size selection are performed as part of the clustering process, and introduces heuristics in the two primary model selection steps.
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On the Quantum versus Classical Learnability of Discrete Distributions
TL;DR: The primary result is the explicit construction of a class of discrete probability distributions which, under the decisional Diffie-Hellman assumption, is provably not efficiently PAC learnable by a classical generative modelling algorithm, but for which an efficient quantum learner is constructed.
Book
Grammatical Inference for Computational Lingustics
TL;DR: This book provides a thorough introduction to the subfield of theoretical computer science known asgrammatical inference from a computational linguistic perspective and summarizes the major lessons and open questions that grammatical inference brings to computational linguistics.
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Input Strictly Local opaque maps
TL;DR: It is discussed how ISL maps are restrictive in their typological predictions, have guaranteed learning results and known methods for generation and recognition, and thus compare favourably to rule-based and constraint-based approaches to these interactions.
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Prediction of MHC class I binding peptides by a query learning algorithm based on hidden markov models.
TL;DR: After 7 rounds of active learning with 181 peptides in all, predictive performance of the algorithm surpassed the so far bestperforming matrix based prediction and by combining the both methods binder peptides could be predicted with84% accuracy.