作者: REIGN 時間: 2025-3-21 21:55
Probabilistic Inductive Logic Programming intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductive logic programming and sketch how it can be extended作者: MELD 時間: 2025-3-22 02:09 作者: 收養(yǎng) 時間: 2025-3-22 04:54 作者: OMIT 時間: 2025-3-22 10:45
Gerold Ambrosius,Hartmut Kaelbleed from relational databases using inductive logic programming and iterative optimization of a pseudo-likelihood measure. Inference is performed by Markov chain Monte Carlo over the minimal subset of the ground network required for answering the query. Experiments in a real-world university domain illustrate the promise of this approach.作者: 變形 時間: 2025-3-22 13:13 作者: Isolate 時間: 2025-3-22 20:40 作者: 等級的上升 時間: 2025-3-22 22:13
https://doi.org/10.1007/978-3-658-05277-5 with probabilistic methods..More precisely, we outline three classical settings for inductive logic programming, namely ., ., and ., and show how they can be used to learn different types of probabilistic representations.作者: Awning 時間: 2025-3-23 04:08 作者: 大喘氣 時間: 2025-3-23 09:08 作者: cathartic 時間: 2025-3-23 11:18
0302-9743 ing. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of 978-3-540-23356-5978-3-540-30215-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: RADE 時間: 2025-3-23 17:30
Conference proceedings 2004ata, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of 作者: 比目魚 時間: 2025-3-23 18:44 作者: Melanoma 時間: 2025-3-23 22:11
978-3-540-23356-5Springer-Verlag Berlin Heidelberg 2004作者: lethargy 時間: 2025-3-24 03:13 作者: tic-douloureux 時間: 2025-3-24 09:05
Applications of Regularized Least Squares to Classification ProblemsWe present a survey of recent results concerning the theoretical and empirical performance of algorithms for learning regularized least-squares classifiers. The behavior of these family of learning algorithms is analyzed in both the statistical and the worst-case (individual sequence) data-generating models.作者: Ethics 時間: 2025-3-24 12:43
String Pattern Discovery series of our works concerning with the string pattern discovery. It includes theoretical analyses of learnabilities of some pattern classes, as well as development of practical data structures which support efficient string processing.作者: perimenopause 時間: 2025-3-24 18:48 作者: Brain-Imaging 時間: 2025-3-24 21:51
Shoham Ben-David,John Case,Akira MaruokaIncludes supplementary material: 作者: receptors 時間: 2025-3-24 23:10
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/152986.jpg作者: Efflorescent 時間: 2025-3-25 06:51
Der Bologna-Prozess als Politiknetzwerk series of our works concerning with the string pattern discovery. It includes theoretical analyses of learnabilities of some pattern classes, as well as development of practical data structures which support efficient string processing.作者: 分期付款 時間: 2025-3-25 08:12
https://doi.org/10.1007/978-3-658-05277-5 intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductive logic programming and sketch how it can be extended作者: STRIA 時間: 2025-3-25 15:41 作者: Benzodiazepines 時間: 2025-3-25 19:15
Gerold Ambrosius,Hartmut Kaelblerecently have we begun to attempt to achieve all three at once. In this talk, I describe Markov logic, a representation that combines the full power of first-order logic and probabilistic graphical models, and algorithms for learning and inference in it. Syntactically, Markov logic is first-order lo作者: 弓箭 時間: 2025-3-25 21:45
Algorithmic Learning Theory978-3-540-30215-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 消毒 時間: 2025-3-26 02:21
Der Bologna-Prozess als Politiknetzwerk series of our works concerning with the string pattern discovery. It includes theoretical analyses of learnabilities of some pattern classes, as well as development of practical data structures which support efficient string processing.作者: 完成才能戰(zhàn)勝 時間: 2025-3-26 08:15
Gerold Ambrosius,Hartmut Kaelbletion. Methods using the minimum description length principle are given for fitting such models to training data. Possible applications of the models are delineated, and some preliminary analysis results on real sets of haplotypes are reported, demonstrating the potential of our methods.作者: overrule 時間: 2025-3-26 09:03
8樓作者: larder 時間: 2025-3-26 16:41
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