Machine Learning - Volume II

Michalski, Ryszard S., Jaime G. Carbonell und Tom M. Mitchell:

ISBN 10: 0934613001 / ISBN 13: 9780934613002

Edité par Morgan Kaufmann Publishers, 1986

A propos de cet article

X, 1 Blatt, 738 Seiten Innen sauberer, guter Zustand. Hardcover, Pappeinband, mit den üblichen Bibliotheks-Markierungen, Stempeln und Einträgen, innen wie außen, siehe Bilder. Ecken und Kanten etwas berieben und bestoßen. Oberes Kapital bestoßen. - An Artificial Intelligence Approach, Volume II. RW-SB-01E|A64 Sprache: Englisch Gewicht in Gramm: 1170. N° de réf. du vendeur 2026990

Détails bibliographiques

Titre : Machine Learning - Volume II

Éditeur : Morgan Kaufmann Publishers

Date d'édition : 1986

Reliure : 24 x 15,5 cm

Etat : Gut

Edition : 1. Auflage.

A propos de ce titre

Synopsis :

Book by Michalski Ryszard S Carbonell Jaime G Mitchell Tom

Présentation de l'éditeur:

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems-one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS).
This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.