Algorithmic Learning Theory

12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001. Proceedings.

Naoki Abe, Roni Khardon, Thomas Zeugmann (Herausgeber)

Buch | Softcover

XII, 388 Seiten

2001 | 2001
Springer Berlin (Verlag)
978-3-540-42875-6 (ISBN)

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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).

Editors' Introduction.- Editors' Introduction.- Invited Papers.- The Discovery Science Project in Japan.- Queries Revisited.- Robot Baby 2001.- Discovering Mechanisms: A Computational Philosophy of Science Perspective.- Inventing Discovery Tools: Combining Information Visualization with Data Mining.- Complexity of Learning.- On Learning Correlated Boolean Functions Using Statistical Queries (Extended Abstract).- A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm.- Minimizing the Quadratic Training Error of a Sigmoid Neuron Is Hard.- Support Vector Machines.- Learning of Boolean Functions Using Support Vector Machines.- A Random Sampling Technique for Training Support Vector Machines.- New Learning Models.- Learning Coherent Concepts.- Learning Intermediate Concepts.- Real-Valued Multiple-Instance Learning with Queries.- Online Learning.- Loss Functions, Complexities, and the Legendre Transformation.- Non-linear Inequalities between Predictive and Kolmogorov Complexities.- Inductive Inference.- Learning by Switching Type of Information.- Learning How to Separate.- Learning Languages in a Union.- On the Comparison of Inductive Inference Criteria for Uniform Learning of Finite Classes.- Refutable Inductive Inference.- Refutable Language Learning with a Neighbor System.- Learning Recursive Functions Refutably.- Refuting Learning Revisited.- Learning Structures and Languages.- Efficient Learning of Semi-structured Data from Queries.- Extending Elementary Formal Systems.- Learning Regular Languages Using RFSA.- Inference of ?-Languages from Prefixes.

Erscheint lt. Verlag	7.11.2001
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XII, 388 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 235 mm
Gewicht	558 g
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Algorithm analysis and problem complexity • Algorithmic Learning • Algorithmic Learning Theory • algorithms • Complexity • Computational Learning • concept learning • Discovery Science • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Inductive Inference • learning • Learning Algorithms • Learning theory • machine learning • Neural Network Learning • Statistical Learning • Support Vector Machine • Support Vector Machines
ISBN-10	3-540-42875-5 / 3540428755
ISBN-13	978-3-540-42875-6 / 9783540428756
Zustand	Neuware