Cantitate/Preț
Produs
Total produse
Vezi coșul
Algorithmic Learning for Knowledge-Based Systems: GOSLER Final Report de Klaus P. Jantke

Algorithmic Learning for Knowledge-Based Systems: GOSLER Final Report: Lecture Notes in Computer Science, cartea 961

Editat de Klaus P. Jantke, Steffen Lange
en Limba Engleză Paperback – 9 aug 1995
This book is the final report on a comprehensive basic research project, named GOSLER on algorithmic learning for knowledge-based systems supported by the German Federal Ministry of Research and Technology during the years 1991 - 1994. This research effort was focused on the study of fundamental learnability problems integrating theoretical research with the development of tools and experimental investigation.
The contributions by 11 participants in the GOSLER project is complemented by contributions from 23 researchers from abroad. Thus the volume provides a competent introduction to algorithmic learning theory.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 34395 lei

Preț vechi: 42993 lei
-20% Nou

Puncte Express: 516
Preț estimativ în valută:
6583€ • 6847$ • 5517£

Carte tipărită la comandă

Livrare economică 13-27 martie

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783540602170
ISBN-10: 3540602178
Pagini: 532
Ilustrații: X, 522 p.
Dimensiuni: 155 x 235 x 28 mm
Greutate: 0.74 kg
Ediția:1995
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Learning and consistency.- Error detecting in inductive inference.- Learning from good examples.- Towards reduction arguments for FINite learning.- Not-so-nearly-minimal-size program inference (preliminary report).- Optimization problem in inductive inference.- On identification by teams and probabilistic machines.- Topological considerations in composing teams of learning machines.- Probabilistic versus deterministic memory limited learning.- Classification using information.- Classifying recursive predicates and languages.- A guided tour across the boundaries of learning recursive languages.- Pattern inference.- Inductive learning of recurrence-term languages from positive data.- Learning formal languages based on control sets.- Learning in case-based classification algorithms.- Optimal strategies — Learning from examples — Boolean equations.- Feature construction during tree learning.- On lower bounds for the depth of threshold circuits with weights from {?1,0,+1}.- Structuring neural networks and PAC-Learning.- Inductive synthesis of rewrite programs.- TLPS — A term rewriting laboratory (not only) for experiments in automatic program synthesis.- GoslerP — A logic programming tool for inductive inference.