Titlebook: ;

Nonporous

Learning and Mathematicshat our models generally do not represent learning a concept as an incremental addition to a rich collection of related concepts. This raises the question of how to make a good model of a “rich collection of related concepts.” Rather than start by trying to make a general model, or adapting existing

使糾纏

Learning Finite-State Models for Machine Translationful, recursive models can be used to account for more complex mappings, it has been argued that the input-output relations underlying most usual natural language pairs are essentially rational. Moreover, the relative simplicity of these mappings has recently lead to the development of techniques for

分發(fā)

The Omphalos Context-Free Grammar Learning Competition. The competition was created in an effort to promote the development of new and better grammatical inference algorithms for context-free languages, to provide a forum for the comparison of different grammatical inference algorithms and to gain insight into the current state-of-the-art of context-fr

PLE

Mutually Compatible and Incompatible Merges for the Search of the Smallest Consistent DFAng problems. EDSM, however, often does not converge to the target DFA and, in the case of sparse training data, does not converge at all. In this paper we argue that is partially due to the particular heuristic used in EDSM and also to the greedy search strategy employed in EDSM. We then propose a n

FAZE

Faster Gradient Descent Training of Hidden Markov Models, Using Individual Learning Rate Adaptationdescent method for Conditional Maximum Likelihood (CML) training of HMMs, which significantly outperforms traditional gradient descent. Instead of using fixed learning rate for every adjustable parameter of the HMM, we propose the use of independent learning rate/step-size adaptation, which has been

投射

態(tài)度暖昧

Learnability of Pregroup Grammarstics. In a first theoretical approach, we provide learnability and non-learnability results in the sense of Gold for subclasses of Pregroup Grammars. In a second more practical approach, we propose an acquisition algorithm from a special kind of input called Feature-tagged Examples, that is based on

explicit

A Markovian Approach to the Induction of Regular String Distributionsmodels (POMMs) is introduced. POMMs form a particular case of HMMs where any state emits a single letter with probability one, but several states can emit the same letter. It is shown that any HMM can be represented by an equivalent POMM. The proposed induction algorithm aims at finding a POMM fitti

使成整體

Learning Node Selecting Tree Transducer from Completely Annotated Examplesrees automatically from examples. We introduce . (.) and show how to induce deterministic .s in polynomial time from completely annotated examples. We have implemented learning algorithms for .s, started applying them to Web information extraction, and present first experimental results.

Cytology

Identifying Clusters from Positive Data. considered is given by a numbering ..,..,... of nonempty subsets of ? or ?. which is also used as a hypothesis space. A clustering task is a finite and nonempty set of indices of pairwise disjoint sets. The class . is said to be clusterable if there is an algorithm which, for every clustering task