Blick ins Buch

Algorithmic Learning Theory

14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings

Ricard Gavaldà, Klaus P. Jantke, Eiji Takimoto (Herausgeber)

Buch | Softcover

XII, 320 Seiten

2003
Springer Berlin (Verlag)
978-3-540-20291-2 (ISBN)

Lese- und Medienproben

Inhaltsverzeichnis (PDF)

Artikel merken

This volume contains the papers presented at the 14th Annual Conference on Algorithmic Learning Theory (ALT 2003), which was held in Sapporo (Japan) duringOctober17 19,2003. Themainobjectiveoftheconferencewastoprovide an interdisciplinary forum for discussing the theoretical foundations of machine learning as well as their relevance to practical applications. The conference was co-locatedwiththe6thInternationalConferenceonDiscoveryScience(DS2003). The volume includes 19 technical contributions that were selected by the program committee from 37 submissions. It also contains the ALT 2003 invited talks presented by Naftali Tishby (Hebrew University, Israel) on E?cient Data Representations that Preserve Information, by Thomas Zeugmann (University of Lub eck, Germany) on Can Learning in the Limit be Done E?ciently? , and by Genshiro Kitagawa (Institute of Statistical Mathematics, Japan) on S- nal Extraction and Knowledge Discovery Based on Statistical Modeling (joint invited talk with DS 2003). Furthermore, this volume includes abstracts of the invitedtalksforDS2003presentedbyThomasEiter(ViennaUniversityofTe- nology, Austria) on Abduction and the Dualization Problem and by Akihiko Takano (National Institute of Informatics, Japan) on Association Computation for Information Access. The complete versions of these papers were published in the DS 2003 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2843). ALT has been awarding theE. MarkGoldAward for the most outstanding paper by a student author since 1999. This year the award was given to Sandra Zilles for her paper Intrinsic Complexity of Uniform Learning. This conference was the 14th in a series of annual conferences established in 1990. ContinuationoftheALTseriesissupervisedbyitssteeringcommittee,c- sisting of: Thomas Zeugmann (Univ.

Invited Papers.- Abduction and the Dualization Problem.- Signal Extraction and Knowledge Discovery Based on Statistical Modeling.- Association Computation for Information Access.- Efficient Data Representations That Preserve Information.- Can Learning in the Limit Be Done Efficiently?.- Inductive Inference.- Intrinsic Complexity of Uniform Learning.- On Ordinal VC-Dimension and Some Notions of Complexity.- Learning of Erasing Primitive Formal Systems from Positive Examples.- Changing the Inference Type - Keeping the Hypothesis Space.- Learning and Information Extraction.- Robust Inference of Relevant Attributes.- Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables.- Learning with Queries.- On the Learnability of Erasing Pattern Languages in the Query Model.- Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries.- Learning with Non-linear Optimization.- Kernel Trick Embedded Gaussian Mixture Model.- Efficiently Learning the Metric with Side-Information.- Learning Continuous Latent Variable Models with Bregman Divergences.- A Stochastic Gradient Descent Algorithm for Structural Risk Minimisation.- Learning from Random Examples.- On the Complexity of Training a Single Perceptron with Programmable Synaptic Delays.- Learning a Subclass of Regular Patterns in Polynomial Time.- Identification with Probability One of Stochastic Deterministic Linear Languages.- Online Prediction.- Criterion of Calibration for Transductive Confidence Machine with Limited Feedback.- Well-Calibrated Predictions from Online Compression Models.- Transductive Confidence Machine Is Universal.- On the Existence and Convergence of Computable Universal Priors.

Erscheint lt. Verlag	7.10.2003
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XII, 320 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 233 mm
Gewicht	467 g
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	algorithm • Algorithm analysis and problem complexity • Algorithmic Learning • Algorithmic Learning Theory • algorithms • Complexity • Computational Learning • concept learning • Data Mining • Heuristics • Inductive Inference • Knowledge Discovery • Learning Algorithms • machine learning • Modeling • Neural Network Learning • Optimization • Statistical Learning • Support Vector Machines • Variable
ISBN-10	3-540-20291-9 / 3540202919
ISBN-13	978-3-540-20291-2 / 9783540202912
Zustand	Neuware