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Structural Complexity I (Paperback, 2nd ed. 1995. Softcover reprint of the original 2nd ed. 1995): Jose L. Balcazar, Josep... Structural Complexity I (Paperback, 2nd ed. 1995. Softcover reprint of the original 2nd ed. 1995)
Jose L. Balcazar, Josep Diaz, Joaquim Gabarro
R1,535 Discovery Miles 15 350 Ships in 10 - 15 working days

In the six years since the first edition of this book was published, the field of Structural Complexity has grown quite a bit. However, we are keeping this volume at the same basic level that it had in the first edition, and the only new result incorporated as an appendix is the closure under complementation of nondeterministic space classes, which in the previous edition was posed as an open problem. This result was already included in our Volume II, but we feel that due to the basic nature of the result, it belongs to this volume. There are of course other important results obtained during these last six years. However, as they belong to new areas opened in the field they are outside the scope of this fundamental volume. Other changes in this second edition are the update of some Bibliograph ical Remarks and references, correction of many mistakes and typos, and a renumbering of the definitions and results. Experience has shown us that this new numbering is a lot more friendly, and several readers have confirmed this opinion. For the sake of the reader of Volume II, where all references to Volume I follow the old numbering, we have included here a table indicating the new number corresponding to each of the old ones."

Structural Complexity II (Paperback, Softcover reprint of the original 1st ed. 1990): Jose L. Balcazar, Josep Diaz, Joaquim... Structural Complexity II (Paperback, Softcover reprint of the original 1st ed. 1990)
Jose L. Balcazar, Josep Diaz, Joaquim Gabarro
R3,227 Discovery Miles 32 270 Ships in 10 - 15 working days

This is the second volume of a two volume collection on Structural Complexity. This volume assumes as a prerequisite knowledge about the topics treated in Volume I, but the present volume itself is nearly self-contained. As in Volume I, each chapter of this book ends with a section entitled "Bibliographical Remarks", in which the relevant references for the chapter are briefly commented upon. These sections might also be of interest to those wanting an overview of the evolution of the field, as well as relevant related results which are not included in the text. Each chapter includes a section of exercises. The reader is encouraged to spend some time on them. Some results presented as exercises are occasionally used later in the text. A reference is provided for the most interesting and for the most useful exercises. Some exercises are marked with a * to indicate that, to the best knowledge of the authors, the solution has a certain degree of difficulty. Many topics from the field of Structural Complexity are not treated in depth, or not treated at all. The authors bear all responsibility for the choice of topics, which has been made based on the interest of the authors on each topic. Many friends and colleagues have made suggestions or corrections. In partic ular we would like to express our gratitude to Richard Beigel, Ron Book, Rafael Casas, Jozef Gruska, Uwe Schoning, Pekka Orponen, and Osamu Watanabe.

Algorithmic Learning Theory - 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings... Algorithmic Learning Theory - 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings (Paperback, 2006 ed.)
Jose L. Balcazar, Philip M. Long, Frank Stephan
R1,730 Discovery Miles 17 300 Ships in 10 - 15 working days

This book constitutes the refereed proceedings of the 17th International Conference on Algorithmic Learning Theory, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the 9th International Conference on Discovery Science, DS 2006.

The 24 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 53 submissions. The papers are dedicated to the theoretical foundations of machine learning.

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