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Titlebook: Inductive Logic Programming; 16th International C Stephen Muggleton,Ramon Otero,Alireza Tamaddoni-Ne Conference proceedings 2007 Springer-V

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書(shū)目名稱(chēng)Inductive Logic Programming
副標(biāo)題16th International C
編輯Stephen Muggleton,Ramon Otero,Alireza Tamaddoni-Ne
視頻videohttp://file.papertrans.cn/464/463904/463904.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Inductive Logic Programming; 16th International C Stephen Muggleton,Ramon Otero,Alireza Tamaddoni-Ne Conference proceedings 2007 Springer-V
描述The inherent dangers of change are often summed up in the misquoted Chinese curse “May you live in interesting times.” The submission procedure for the 16th International Conference of Inductive Logic Programming (ILP 2006) was a radical (hopefully interesting but not cursed) departure from previous years. Submissions were requested in two phases. The ?rst phase involved submission of short papers (three pages) which were then presented at the conference and included in a short papers proceedings. In the second phase, reviewers selected papersforlongpapersubmission(15pagesmaximum).Thesewerethenassessed by the same reviewers, who then decided which papers to include in the journal special issue and proceedings. In the ?rst phase there were a record 77 papers, comparedto the usual20 orso long papersofpreviousyears.Eachpaper was- viewed by three reviewers. Out of these, 71 contributors were invited to submit long papers. Out of the long paper submissions, 7 were selected for the - chine Learning Journal special issue and 27 were accepted for the proceedings. In addition, two papers were nominated by Program Committee referees for the applications prize and two for the theory prize. Th
出版日期Conference proceedings 2007
關(guān)鍵詞Bayesian networks; algorithm; algorithmic learning; algorithms; bioinformatics; classifier systems; comple
版次1
doihttps://doi.org/10.1007/978-3-540-73847-3
isbn_softcover978-3-540-73846-6
isbn_ebook978-3-540-73847-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
The information of publication is updating

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ALLPAD: Approximate Learning of Logic Programs with Annotated Disjunctionsin order to tackle real world learning problems more effectively. This is achieved by looking for an approximate solution rather than a perfect one. ALLPAD has been tested on the problem of classifying proteins according to their tertiary structure and the results compare favorably with most other approaches.
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Revising Probabilistic Prolog ProgramsThe ProbLog (probabilistic prolog) language has been introduced in [1], where various algorithms have been developed for solving and approximating ProbLog queries. Here, we define and study the problem of revising ProbLog theories from examples.
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978-3-540-73846-6Springer-Verlag Berlin Heidelberg 2007
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Margin-Based First-Order Rule Learninge-art rule learning approaches [1], we therefore assign weights to the rules. In this way, a rule set represents a linear classifier and one can optimize . optimization criteria, essentially reducing the misclassification error on noisy data. Since we aim at comprehensible models, we employ margins
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