Titlebook: Machine Learning: ECML 2007; 18th European Confer Joost N. Kok,Jacek Koronacki,Andrzej Skowron Conference proceedings 2007 Springer-Verlag

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Learning Partially Observable Markov Models from First Passage Timesraphical models equivalent to Hidden Markov Models (HMMs). The model structure is built to support the First Passage Times (FPT) dynamics observed in the training sample. We argue that the FPT in POMMs are closely related to the model structure. Starting from a standard Markov chain, states are iter

偽造者

Context Sensitive Paraphrasing with a Global Unsupervised Classifier templates that can replace other patterns or templates in . context, but we are attempting to make decisions for a . context. In this paper we develop a global classifier that takes a word . and its context, along with a candidate word ., and determines whether . can replace . in the given context

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Dual Strategy Active Learningtion to multi-factor methods with learn-once-use-always model parameters. This paper proposes a dynamic approach, called DUAL, where the strategy selection parameters are adaptively updated based on estimated future residual error reduction after each actively sampled point. The objective of dual is

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Decision Tree Instability and Active Learninge – they can produce drastically different hypotheses from training sets that differ just slightly. This instability undermines the objective of extracting knowledge from the trees. In this paper, we study the instability of the C4.5 decision tree learner in the context of active learning. We introd

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Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Suently provided in the form of pairwise must-link and cannot-link constraints. While the incorporation of pairwise supervision has the potential to improve clustering accuracy, the composition and cardinality of the constraint sets can significantly impact upon the level of improvement. We demonstra

Introduction

The Cost of Learning Directed Cuts setting in which the directed cut is fixed. However, even in this setting learning is not possible without in the worst case needing the labels for the whole vertex set. By considering the size of the minimum path cover as a fixed parameter, we derive positive learnability results with tight perfor

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