• Alchemy

  • Referenced in 11 articles [sw16040]
  • providing a series of algorithms for statistical relational learning and probabilistic logic inference, based...
  • kLog

  • Referenced in 4 articles [sw10403]
  • introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does ... builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming ... technique we call graphicalization: the relational representation is first transformed into a graph -- in particular ... range of tasks that has made statistical relational learning so popular, including classification, regression, multitask...
  • KReator

  • Referenced in 3 articles [sw06946]
  • probabilistic inductive logic programming (or statistical relational learning) aims at applying probabilistic methods of inference ... interface for representing, reasoning and learning with different relational probabilistic approaches. It is a general ... probabilistic inductive logic programming and statistical relational learning. Currently, KReator implements Bayesian logic programs, Markov ... probabilistic inductive logic programming and statistical relational learning and illustrate the usage of KReator...
  • Persistence Landscape

  • Referenced in 13 articles [sw21260]
  • easily combined with tools from statistics and machine learning. We give efficient algorithms for calculating ... related procedures. These are intended to facilitate the combination of statistics and machine learning with...
  • HyPER

  • Referenced in 2 articles [sw23917]
  • show how a recently introduced statistical relational learning framework can be used to develop...
  • foxPSL

  • Referenced in 2 articles [sw13725]
  • leading formalisms of statistical relational learning, a recently developed field of machine learning that aims...
  • CORN

  • Referenced in 6 articles [sw15436]
  • effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate...
  • ProPPR

  • Referenced in 1 article [sw32915]
  • scalable inference. A key challenge in statistical relational learning is to develop a semantically rich...
  • Boostr

  • Referenced in 1 article [sw16041]
  • Boostr: Boosted STatistical Relational Learning. Boostr learns the structure of Relational Dependency Networks (RDNs...
  • LS-SVMlab

  • Referenced in 26 articles [sw07367]
  • been introduced within the context of statistical learning theory and structural risk minimization ... linear KKT systems. LS-SVMs are closely related to regularization networks and Gaussian processes...
  • MBT

  • Referenced in 6 articles [sw08004]
  • shares this advantage with other statistical or machine learning approaches. Additional advantages specific ... relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities ... words without morphological analysis, and (vii) fast learning and tagging. In this paper we show ... with that of known statistical approaches, and with attractive space and time complexity properties when...
  • EMILeA-stat

  • Referenced in 1 article [sw34663]
  • teaching and learning statistics in secondary schools. Related contents, interactive visualizations, and didactical aspects...
  • SeqROCTM

  • Referenced in 1 article [sw35109]
  • them. To model such relation in the context of statistical learning in neuroscience...
  • FACTORIE

  • Referenced in 13 articles [sw08947]
  • complex relational data. The power in relational models is in their repeated structure and tied ... model structure, inference, and learning. By combining the traditional, declarative, statistical semantics of factor graphs...
  • DPPy

  • Referenced in 6 articles [sw27047]
  • tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs ... short survey of DPPs and relates each mathematical property with DPPy objects...
  • FLAM

  • Referenced in 3 articles [sw33776]
  • well as in statistical contexts such as uncertainty quantification and machine learning. They have appeared ... literature under an assortment of related names and frameworks (e.g., H-, H2-, FMM, HODLR...
  • EMILE

  • Referenced in 4 articles [sw19074]
  • that have this substitutionability relation. If there exists enough statistical evidence for the existence ... learning capacities of the EMILE 4.1 algorithm. The EMILE algorithm is relatively scalable...
  • Fathon

  • Referenced in 1 article [sw08389]
  • creatively explore ideas in mathematics, statistics, and science. Relate your studies to real-world examples ... before! Fathom 2 enhances learning from algebra to calculus, statistics, and beyond. Get excited about...
  • DR-ABC

  • Referenced in 3 articles [sw24742]
  • statistically efficient way using the random Fourier features framework for large-scale kernel learning ... framework shows superior performance when compared to related methods on toy and real-world problems...
  • netgwas

  • Referenced in 3 articles [sw21512]
  • They are widely used in statistics and machine learning particularly to analyze biological networks ... designed for accomplishing three important, and inter-related, goals in genetics: linkage map construction, reconstructing ... paper includes a brief overview of the statistical methods which have been implemented...