作者: 肥料 時間: 2025-3-21 21:41
978-3-540-78651-1Springer-Verlag Berlin Heidelberg 2008作者: 縮短 時間: 2025-3-22 01:14
Probabilistic Inductive Logic Programming978-3-540-78652-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: fiscal 時間: 2025-3-22 05:28
0302-9743 Overview: 978-3-540-78651-1978-3-540-78652-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 實施生效 時間: 2025-3-22 12:27
https://doi.org/10.1007/978-3-540-78652-8Bayesian networks; Kernel; algorithmic learning; classifier systems; clustering; computational biology; co作者: 左右連貫 時間: 2025-3-22 16:25
CLP(,): Constraint Logic Programming for Probabilistic Knowledgedatabase or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(.) programs. An implementation of CLP(.) is publicly available as part of YAP Prolog at ..作者: ascend 時間: 2025-3-22 17:50
Probabilistic Inductive Logic Programmingto the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely ., ., and ., and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.作者: paleolithic 時間: 2025-3-22 23:06
The Independent Choice Logic and Beyondural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity.作者: innate 時間: 2025-3-23 01:25
Probabilistic Logic Learning from Haplotype Datatructions from different sources, which can increase accuracy and robustness of reconstruction estimates. Finally, techniques for discovering the structure in haplotype data at the level of haplotypes and population are discussed.作者: 種類 時間: 2025-3-23 06:49
Relational Sequence Learningg and describes several techniques tailored towards realizing this, such as local pattern mining techniques, (hidden) Markov models, conditional random fields, dynamic programming and reinforcement learning.作者: 預(yù)感 時間: 2025-3-23 11:00
Learning with Kernels and Logical Representationse is obtained by recording the execution trace of a program expressing background knowledge. In ., features are directly associated with mereotopological relations. Finally, in ., features correspond to the truth values of clauses dynamically generated by a greedy search algorithm guided by the empirical risk.作者: 售穴 時間: 2025-3-23 16:21
Markov Logicrning algorithms are based on the conjugate gradient algorithm, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to problems in entity resolution, link prediction, information extraction and others, and is the basis of the open-source Alchemy system.作者: 航海太平洋 時間: 2025-3-23 18:19 作者: dictator 時間: 2025-3-24 00:21 作者: 辯論的終結(jié) 時間: 2025-3-24 03:37 作者: DEMN 時間: 2025-3-24 09:07
A Behavioral Comparison of Some Probabilistic Logic Modelss. We further demonstrate that BLPs (resp. SLPs) can encode the relational semantics of PRMs (resp. SRMs). Whenever applicable, we provide inter-translation algorithms, present their soundness and give worked examples.作者: concentrate 時間: 2025-3-24 13:40 作者: Mettle 時間: 2025-3-24 18:30
Kristian Kersting,Luc De Raedt,Bernd Gutmann,Andreas Karwath,Niels Landwehrity submissions that could not be accommodated in the program; hopefully they will be published elsewhere. ThecontinuedsuccessoftheCCconferenceserieswouldnotbep978-3-540-21297-3978-3-540-24723-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 牌帶來 時間: 2025-3-24 22:14 作者: minion 時間: 2025-3-25 00:26 作者: 芭蕾舞女演員 時間: 2025-3-25 07:16 作者: JOT 時間: 2025-3-25 11:17 作者: multiply 時間: 2025-3-25 11:47 作者: 省略 時間: 2025-3-25 16:40
Vítor Santos Costa,David Page,James Cussenstion. To our knowledge, SPRP is the first compiler technique to speculatively parallelise recursive procedures this way. Compared with existing static thread prediction techniques, dynamic thread prediction reduces the number of useless threads spawned, and consequently, misspeculation overhead incu作者: 驚惶 時間: 2025-3-25 23:27
Luc De Raedt,Kristian KerstingW and superscalar processors. Since the amount of ILP is usually fixed to a small number, four — eight, using state-of-the-art software pipelining scheduling techniques, modern compilers have been able to schedule instructions in a small window of successive iterations and keep the machine resources作者: PLAYS 時間: 2025-3-26 00:13
Kristian Kersting,Luc De Raedt,Bernd Gutmann,Andreas Karwath,Niels Landwehrn (CC 2004). CC continues to provide an exciting forum for researchers, educators, and practitioners to exchange ideas on the latest developments in compiler te- nology, programming language implementation, and language design. The c- ference emphasizes practical and experimental work and invites co作者: Restenosis 時間: 2025-3-26 04:28
Paolo Frasconi,Andrea Passerinit Linear Scan algorithms while delivering performance that can match or surpass that of Graph Coloring. Specifically, this paper makes the following contributions:.– We highlight three fundamental . in using Graph Coloring as a foundation for global register allocation, and introduce a . Extended Li作者: 脖子 時間: 2025-3-26 11:56 作者: Esophagitis 時間: 2025-3-26 14:17
Taisuke Sato,Yoshitaka Kameyaofiling, reuse distance analysis has shown much promise in predicting data locality for a program using inputs other than the profiled ones. Both whole-program and instruction-based locality can be accurately predicted by reuse distance analysis..Reuse distance analysis abstracts a cluster of memory作者: Eeg332 時間: 2025-3-26 18:57
Vítor Santos Costa,David Page,James Cussens focused mainly on parallelising loops. Recursive procedures, which are also frequently used in real-world applications, have attracted much less attention. Moreover, the parallel threads in prior work are statically predicted and spawned. In this paper, we introduce a new compiler technique, called作者: nocturia 時間: 2025-3-26 23:56
Probabilistic Inductive Logic Programmingintegration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far..In this 作者: GUEER 時間: 2025-3-27 03:55
Relational Sequence Learningtly be represented using relational atoms. Applying traditional sequential learning techniques to such relational sequences requires one either to ignore the internal structure or to live with a combinatorial explosion of the model complexity. This chapter briefly reviews relational sequence learnin作者: Heart-Rate 時間: 2025-3-27 06:52
Learning with Kernels and Logical Representationsntation of data and background knowledge are used to form a kernel function, enabling us to subsequently apply a number of kernel-based statistical learning algorithms. Different representational frameworks and associated algorithms are explored in this chapter. In ., the representation of an exampl作者: SEED 時間: 2025-3-27 11:59
Markov Logicd relational logic. Markov logic accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Lea作者: 就職 時間: 2025-3-27 14:46 作者: 彩色的蠟筆 時間: 2025-3-27 19:28
CLP(,): Constraint Logic Programming for Probabilistic Knowledgebles, are represented by terms built from Skolem functors. The CLP(.) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only
