Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	September 2004
Genre:	EDV / Informatik
	Algorithm Analysis and Problem Complexity / Algorithmen und Datenstrukturen / Algorithms / Algorithms & data structures / Artificial Intelligence / C / Computation by Abstract Devices / computer science / Computers / Formal Languages and Automata Theory / Mathematical logic / Mathematical Logic and Formal Languages / Mathematical theory of computation / Natural language & machine translation / Natural language processing (Computer science) / Natural Language Processing (NLP) / Natürliche Sprachen und maschinelle Übersetzung / Theory of Computation mehr...
ISBN:	9783540233565
EAN-Code:	9783540233565
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Serie:	Lecture Notes in Artificial Intelligence #3244 - Lecture Notes in Computer Science
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	1620 gr
Seiten:	514
Illustration:	XIV, 514 p.
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.