Name: Algorithmic Learning Theory. 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (en Inglés)
Price: 175037.00 COP
Availability: InStock
Author: Shai Ben David;John Case;Akira Maruoka
ISBN: 9783662205204

portada Algorithmic Learning Theory. 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (en Inglés)

Algorithmic Learning Theory. 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Shai Ben David;John Case;Akira Maruoka

Año

2014

Idioma

Inglés

N° páginas

528

Encuadernación

Tapa Blanda

Dimensiones

23.40 x 15.60 x 2.70 cm

ISBN13

9783662205204

Algorithmic Learning Theory. 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (en Inglés)

Shai Ben David;John Case;Akira Maruoka (Autor) · Springer · Tapa Blanda

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Reseña del libro "Algorithmic Learning Theory. 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings (en Inglés)"

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.