Algorithmic Learning Theory

Description

This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25–28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2226).

First Sentence

Formal models of learning reflect a variety of differences in tasks, sources of information, prior knowledge and capabilities of the learner, and criteria of successful performance.

Subjects

Other Editions

• Algorithmic Learning Theory: 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001. Proceedings. (Lecture Notes in Computer Science)PaperbackSpringer2001-12-12
• Algorithmic Learning TheorySpringer2003-01-01
• Algorithmic Learning TheorySpringer2003-01-01
• Algorithmic Learning TheorySpringer2009-01-01
• Algorithmic Learning TheorySpringer2014-01-01