Algorithmic learning theory. 15th international conference, ALT 2004, Padova, Italy, October 2--5, 2004. Proceedings.
The articles of this volume will be reviewed individually. The 14th conference (2003) has been reviewed (see Zbl 1064.68001). Indexed articles: \textit{Shinohara, Ayumi}, String pattern discovery, 1-13 [Zbl 1110.68467] \textit{Cesa-Bianchi, Nicolò}, Applications of regularized least squares to classification problems, 14-18 [Zbl 1110.68435] \textit{De Raedt, Luc; Kersting, Kristian}, Probabilistic inductive logic programming, 19-36 [Zbl 1110.68392] \textit{Koivisto, Mikko; Kivioja, Teemu; Mannila, Heikki; Rastas, Pasi; Ukkonen, Esko}, Hidden Markov modelling techniques for haplotype analysis, 37-52 [Zbl 1110.68453] \textit{Domingos, Pedro}, Learning, logic, and probability: A unified view, 53 [Zbl 1110.68501] \textit{Jain, Sanjay; Kinber, Efim}, Learning languages from positive data and negative counterexamples, 54-68 [Zbl 1110.68397] \textit{Rao, M. R. K. Krishna}, Inductive inference of term rewriting systems from positive data, 69-82 [Zbl 1110.68406] \textit{Martin, Eric; Sharma, Arun; Stephan, Frank}, On the data consumption benefits of accepting increased uncertainty, 83-98 [Zbl 1110.68400] \textit{Lange, Steffen; Zilles, Sandra}, Comparison of query learning and Gold-style learning in dependence of the hypothesis space, 99-113 [Zbl 1110.68399] \textit{Hatano, Kohei; Watanabe, Osamu}, Learning \(r\)-of-\(k\) functions by boosting, 114-126 [Zbl 1110.68395] \textit{Takimoto, Eiji; Koya, Syuhei; Maruoka, Akira}, Boosting based on divide and merge, 127-141 [Zbl 1110.68407] \textit{Miyata, Akinobu; Tarui, Jun; Tomita, Etsuji}, Learning Boolean functions in \(AC^{0}\) on attribute and classification noise, 142-155 [Zbl 1110.68403] \textit{Fiat, Amos; Pechyony, Dmitry}, Decision trees: More theoretical justification for practical algorithms, 156-170 [Zbl 1110.68393] \textit{Ryabko, Daniil}, Application of classical nonparametric predictors to learning conditionally i.i.d. data, 171-180 [Zbl 1110.68465] \textit{Ambroladze, Amiran; Shawe-Taylor, John}, Complexity of pattern classes and Lipschitz property, 181-193 [Zbl 1110.68388] \textit{Balcan, Maria-Florina; Blum, Avrim; Vempala, Santosh}, On kernels, margins, and low-dimensional mappings, 194-205 [Zbl 1110.68431] \textit{Watanabe, Kazuho; Watanabe, Sumio}, Estimation of the data region using extreme-value distributions, 206-220 [Zbl 1110.68478] \textit{Maslov, Victor; V'yugin, Vladimir}, Maximum entropy principle in non-ordered setting, 221-233 [Zbl 1110.68401] \textit{Hutter, Marcus; Muchnik, Andrej}, Universal convergence of semimeasures on individual random sequences, 234-248 [Zbl 1110.68396] \textit{Kalnishkan, Yuri; Vovk, Vladimir; Vyugin, Michael V.}, A criterion for the existence of predictive complexity for binary games, 249-263 [Zbl 1110.68398] \textit{Allenberg-Neeman, Chamy; Neeman, Benny}, Full information game with gains and losses, 264-278 [Zbl 1110.68387] \textit{Hutter, Marcus; Poland, Jan}, Prediction with expert advice by following the perturbed leader for general weights, 279-293 [Zbl 1110.68443] \textit{Poland, Jan; Hutter, Marcus}, On the convergence speed of MDL predictions for Bernoulli sequences, 294-308 [Zbl 1110.68405] \textit{Herbster, Mark}, Relative loss bounds and polynomial-time predictions for the K-LMS-NET algorithm, 309-323 [Zbl 1110.68442] \textit{Simon, Hans Ulrich}, On the complexity of working set selection, 324-337 [Zbl 1110.68468] \textit{List, Nikolas}, Convergence of a generalized gradient selection approach for the decomposition method, 338-349 [Zbl 1110.68457] \textit{Yamazaki, Keisuke; Watanabe, Sumio}, Newton diagram and stochastic complexity in mixture of binomial distributions, 350-364 [Zbl 1110.68481] \textit{Bulatov, Andrei; Chen, Hubie; Dalmau, Víctor}, Learnability of relatively quantified generalized formulas, 365-379 [Zbl 1110.68391] \textit{Mukouchi, Yasuhito; Sato, Masako}, Learning languages generated by elementary formal systems and its application to SH languages, 380-394 [Zbl 1110.68404] \textit{Goldsmith, Judy; Sloan, Robert H.; Szörényi, Balázs; Turán, György}, New revision algorithms, 395-409 [Zbl 1110.68394] \textit{Arias, Marta; Khardon, Roni}, The subsumption lattice and query learning, 410-424 [Zbl 1110.68389] \textit{Matsumoto, Satoshi; Shoudai, Takayoshi}, Learning of ordered tree languages with height-bounded variables using queries, 425-439 [Zbl 1110.68402] \textit{Besombes, Jérôme; Marion, Jean-Yves}, Learning tree languages from positive examples and membership queries, 440-453 [Zbl 1110.68390] \textit{Iglesias, Ana; Martínez, Paloma; Aler, Ricardo; Fernández, Fernando}, Learning content sequencing in an educational environment according to student needs, 454-463 [Zbl 1110.68444] \textit{Tanaka, Toshiyuki}, Statistical learning in digital wireless communications, 464-478 [Zbl 1110.68470] \textit{Kabashima, Yoshiyuki; Uda, Shinsuke}, A BP-based algorithm for performing Bayesian inference in large perceptron-type networks, 479-493 [Zbl 1110.68448] \textit{Opper, Manfred; Winther, Ole}, Approximate inference in probabilistic models, 494-504 [Zbl 1110.68461]
scientific article; zbMATH DE number 2172621