Discovery science. 5th international conference, DS 2002, Lübeck, Germany, November 24--26, 2002. Proceedings (Q1866726)
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scientific article; zbMATH DE number 1898215
| Language | Label | Description | Also known as |
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| English | Discovery science. 5th international conference, DS 2002, Lübeck, Germany, November 24--26, 2002. Proceedings |
scientific article; zbMATH DE number 1898215 |
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Discovery science. 5th international conference, DS 2002, Lübeck, Germany, November 24--26, 2002. Proceedings (English)
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22 April 2003
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The articles of this volume will be reviewed individually. The preceding conference has been reviewed (see Zbl 0983.00058). Indexed articles: \textit{Hayashi, Susumu}, Mathematics based on learning, 1 [Zbl 1024.68535] \textit{Kruse, Rudolf; Borgelt, Christian}, Data mining with graphical models, 2-11 [Zbl 1024.68524] \textit{Shawe-Taylor, John; Williams, Chris; Cristianini, Nello; Kandola, Jaz}, On the eigenspectrum of the Gram matrix and its relationship to the operator eigenspectrum, 12 [Zbl 1024.68538] \textit{Widmer, Gerhard}, In search of the Horowitz factor: Interim report on a musical discovery project, 13-32 [Zbl 1024.68539] \textit{Witten, Ian H.}, Learning structure from sequences, with applications in a digital library, 33 [Zbl 1024.68541] \textit{Palopoli, Luigi; Terracina, Giorgio}, Discovering frequent structured patterns from string databases: An application to biological sequences, 34-46 [Zbl 1024.68578] \textit{Devaney, Judith E.; Hagedorn, John G.}, Discovery in hydrating plaster using machine learning methods, 47-58 [Zbl 1024.68549] \textit{Saito, Kazumi; Bay, Stephen; Langley, Pat}, Revising qualitative models of gene regulation, 59-70 [Zbl 1024.68584] \textit{Ghazizadeh, Shayan; Chawathe, Sudarshan S.}, SEuS: Structure extraction using summaries, 71-85 [Zbl 1024.68555] \textit{Inenaga, Shunsuke; Bannai, Hideo; Shinohara, Ayumi; Takeda, Masayuki; Arikawa, Setsuo}, Discovering best variable-length-don't-care patterns, 86-97 [Zbl 1024.68562] \textit{Ting, Kai Ming}, A study on the effect of class distribution using cost-sensitive learning, 98-112 [Zbl 1024.68586] \textit{Hilario, Melanie}, Model complexity and algorithm selection in classification, 113-126 [Zbl 1024.68558] \textit{Elomaa, Tapio; Lindgren, J. T.}, Experiments with projection learning, 127-140 [Zbl 1024.68550] \textit{Peng, Yonghong; Flach, Peter A.; Soares, Carlos; Brazdil, Pavel}, Improved dataset characterisation for meta-learning, 141-152 [Zbl 1024.68579] \textit{Frank, Eibe; Holmes, Geoffrey; Kirkby, Richard; Hall, Mark}, Racing committees for large datasets, 153-164 [Zbl 1024.68553] \textit{Ferri, C.; Hernández-Orallo, J.; Ramírez-Quintana, M. J.}, From ensemble methods to comprehensible models, 165-177 [Zbl 1024.68552] \textit{Danks, David}, Learning the causal structure of overlapping variable sets, 178-191 [Zbl 1024.68547] \textit{Holeňa, Martin}, Extraction of logical rules from data by means of piecewise-linear neural networks, 192-205 [Zbl 1024.68560] \textit{Saito, Kazumi; Nakano, Ryohei}, Structuring neural networks through bidirectional clustering of weights, 206-219 [Zbl 1024.68585] \textit{Maruyama, Osamu; Shoudai, Takayoshi; Miyano, Satoru}, Toward drawing an atlas of hypothesis classes: Approximating a hypothesis via another hypothesis model, 220-232 [Zbl 1024.68571] \textit{Okada, Takashi}, Datascape survey using the cascade model, 233-246 [Zbl 1024.68577] \textit{Ichise, Ryutaro; Shapiro, Daniel; Langley, Pat}, Learning hierarchical skills from observation, 247-258 [Zbl 1024.68561] \textit{Ruosaari, Salla; Hollmén, Jaakko}, Image analysis for detecting faulty spots from microarray images, 259-266 [Zbl 1024.68582] \textit{de Hoon, Michiel; Imoto, Seiya; Miyano, Satoru}, Inferring gene regulatory networks from time-ordered gene expression data using differential equations, 267-274 [Zbl 1024.68548] \textit{Masotti, Daniele}, DNA-tract curvature profile reconstruction: A fragment flipping algorithm, 275-282 [Zbl 1024.68572] \textit{Kitamoto, Asanobu}, Evolution map: Modeling state transition of typhoon image sequences by spatio-temporal clustering, 283-290 [Zbl 1024.68564] \textit{Halova, Jaroslava; Zak, Premysl; Stopka, Pavel; Yuzuri, Tomoaki; Abe, Yukino; Sakakibara, Kazuhisa; Suezawa, Hiroko; Hirota, Minoru}, Structure-sweetness relationships of aspartame derivatives by GUHA, 291-296 [Zbl 1024.68556] \textit{Fang, Xiaoshan; Sheng, Huanye}, A hybrid approach for Chinese named entity recognition, 297-301 [Zbl 1024.68551] \textit{Moon, Yoo-Jin; Kim, Minkoo; Hwang, Youngho; Kim, Pankoo; Choi, Kijoon}, Extraction of word senses from human factors in knowledge discovery, 302-309 [Zbl 1024.68574] \textit{Li, Fang; Sheng, Huanye; Zhang, Dongmo}, Event pattern discovery from the stock market bulletin, 310-315 [Zbl 1024.68568] \textit{Yang, Jihoon; Park, Sung-Yong}, Email categorization using fast machine learning algorithms, 316-323 [Zbl 1024.68588] \textit{Haraguchi, Makoto; Nakano, Shigetora; Yoshioka, Masaharu}, Discovery of maximal analogies between stories, 324-331 [Zbl 1024.68557] \textit{Yamada, Yasuhiro; Ikeda, Daisuke; Hirokawa, Sachio}, Automatic wrapper generation for multilingual web resources, 332-339 [Zbl 1024.68587] \textit{Bao, Yongguang; Ishii, Naohiro}, Combining multiple k-nearest neighbor classifiers for text classification by reducts, 340-347 [Zbl 1024.68545] \textit{Kontos, John; Elmaoglou, Areti; Malagardi, Ioanna}, ARISTA causal knowledge discovery from texts, 348-355 [Zbl 1024.68565] \textit{Rousseaux, Francis; Bonardi, Alain}, Knowledge discovery as applied to music: Will music web retrieval revolutionize musicology?, 356-363 [Zbl 1024.68581] \textit{Maruster, Laura; Weijters, A. J. M. M.; van der Aalst, W. M. P.; van den Bosch, Antal}, Process mining: Discovering direct successors in process logs, 364-373 [Zbl 1024.68570] \textit{Nakamura, Makoto; Tojo, Satoshi}, The emergence of artificial Creole by the EM algorithm, 374-381 [Zbl 1024.68575] \textit{Lartillot, Olivier}, Generalized musical pattern discovery by analogy from local viewpoints, 382-389 [Zbl 1024.68567] \textit{Fu, Zhiwei}, Using genetic algorithms-based approach for better decision trees: A computational study, 390-397 [Zbl 1024.68554] \textit{Höppner, Frank}, Handling feature ambiguity in knowledge discovery from time series, 398-405 [Zbl 1024.68559] \textit{Zhao, Haiyan; Hu, Zhenjiang; Takeichi, Masato}, A compositional framework for mining longest ranges, 406-413 [Zbl 1024.68589] \textit{Jorge, Alipio; Poças, João; Azevedo, Paulo}, Post-processing operators for browsing large sets of association rules, 414-421 [Zbl 1024.68563] \textit{Matsuda, Takashi; Motoda, Hiroshi; Yoshida, Tetsuya; Washio, Takashi}, Mining patterns from structured data by beam-wise graph-based induction, 422-429 [Zbl 1024.68573] \textit{Krogel, Mark-A.; Wrobel, Stefan}, Feature selection for propositionalization, 430-434 [Zbl 1024.68566] \textit{Narahashi, Masaki; Suzuki, Einoshin}, Subspace clustering based on compressibility, 435-440 [Zbl 1024.68576] \textit{Magnani, Lorenzo; Piazza, Matteo; Dossena, Riccardo}, The extra-theoretical dimension of discovery. Extracting knowledge by abduction, 441-448 [Zbl 1024.68569] \textit{Saito, Hitomi; Miwa, Kazuhisa}, Discovery process on the WWW: Analysis based on a theory of scientific discovery, 449-456 [Zbl 1024.68583] \textit{Piscopo, Carlotta; Birattari, Mauro}, Invention vs. discovery. A critical discussion, 457-462 [Zbl 1024.68580]
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Discovery science
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DS 2002
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Lübeck, Germany
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