Name: Knowledge Acquisition: Selected Research and Commentary: A Special Issue of Machine Learning on Knowledge Acquisition (en Inglés)
SKU: 58124798
Price: 104.94 USD
Availability: InStock
Author: Marcus, Sandra
ISBN: 9781461288213

portada Knowledge Acquisition: Selected Research and Commentary: A Special Issue of Machine Learning on Knowledge Acquisition (en Inglés)

Knowledge Acquisition: Selected Research and Commentary: A Special Issue of Machine Learning on Knowledge Acquisition (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Marcus, Sandra

Idioma

Inglés

N° páginas

152

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x0.9 cm

Peso

0.23 kg.

ISBN13

9781461288213

Categorías

Inteligencia artificial
Jardinería

Knowledge Acquisition: Selected Research and Commentary: A Special Issue of Machine Learning on Knowledge Acquisition (en Inglés)

Marcus, Sandra (Autor) · Springer · Tapa Blanda

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Reseña del libro "Knowledge Acquisition: Selected Research and Commentary: A Special Issue of Machine Learning on Knowledge Acquisition (en Inglés)"

What follows is a sampler of work in knowledge acquisition. It comprises three technical papers and six guest editorials. The technical papers give an in-depth look at some of the important issues and current approaches in knowledge acquisition. The editorials were pro- duced by authors who were basically invited to sound off. I've tried to group and order the contributions somewhat coherently. The following annotations emphasize the connections among the separate pieces. Buchanan's editorial starts on the theme of "Can machine learning offer anything to expert systems?" He emphasizes the practical goals of knowledge acquisition and the challenge of aiming for them. Lenat's editorial briefly describes experience in the development of CYC that straddles both fields. He outlines a two-phase development that relies on an engineering approach early on and aims for a crossover to more automated techniques as the size of the knowledge base increases. Bareiss, Porter, and Murray give the first technical paper. It comes from a laboratory of machine learning researchers who have taken an interest in supporting the development of knowledge bases, with an emphasis on how development changes with the growth of the knowledge base. The paper describes two systems. The first, Protos, adjusts the training it expects and the assistance it provides as its knowledge grows. The second, KI, is a system that helps integrate knowledge into an already very large knowledge base.