• ProbLog

  • Referenced in 82 articles [sw06945]
  • various approximate methods. ProbLog1 also supports parameter learning, in both the learning from entailment ... learning from interpretations setting. ProbLog1 also supports decision-theoretic inference. ProbLog2 allows the user ... evidence). ProbLog2 also supports parameter learning in the learning from interpretations setting...
  • bnlearn

  • Referenced in 56 articles [sw08265]
  • package bnlearn: Bayesian network structure learning, parameter learning and inference. Bayesian network structure learning, parameter ... Search) and hybrid (MMHC and RSMAX2) structure learning algorithms for both discrete and Gaussian networks ... included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional...
  • LIBSVM

  • Referenced in 1088 articles [sw04879]
  • LIBSVM has gained wide popularity in machine learning and many other areas. In this article ... theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail: http://dl.acm.org...
  • BNT

  • Referenced in 69 articles [sw07384]
  • distributions), exact and approximate inference, parameter and structure learning, and static and dynamic models...
  • LibDAI

  • Referenced in 15 articles [sw06422]
  • probability distributions and maximum probability states. Parameter learning is also supported. A feature comparison with...
  • glasso

  • Referenced in 384 articles [sw07432]
  • graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphical ... models for different values of the tuning parameter. Convergence of glasso can be tricky...
  • CTBN-RLE

  • Referenced in 5 articles [sw12961]
  • learning, CTBN-RLE implements structure and parameter learning for both complete and partial data...
  • Evigan

  • Referenced in 5 articles [sw14331]
  • model is a dynamic Bayes network whose parameters are adjusted to maximize the probability ... matches, and splice site predictions; learned parameters encode the relative quality of evidence sources. Since...
  • CONTRAfold

  • Referenced in 8 articles [sw17117]
  • fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods ... statistical learning procedures provide an effective alternative to empirical measurement of thermodynamic parameters...
  • mlr

  • Referenced in 27 articles [sw12357]
  • Machine Learning in R. Interface to a large number of classification and regression techniques, including ... machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering ... general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter...
  • CONTRAlign

  • Referenced in 4 articles [sw08305]
  • extensible and fully automatic framework for parameter learning and protein pairwise sequence alignment using pair...
  • SLEP

  • Referenced in 36 articles [sw13487]
  • SLEP: Sparse Learning with Efficient Projections. Main Features: 1) First-Order Method. At each iteration ... solutions corresponding to a series of regularization parameters by the “warm-start” technique...
  • OKVAR-Boost

  • Referenced in 3 articles [sw24063]
  • operator-valued kernels that simultaneously learns the model parameters, as well as the network structure ... developed to perform the tasks of parameter learning and network inference for the proposed model...
  • PyMix

  • Referenced in 2 articles [sw07781]
  • specific independence structure learning; Partially supervised parameter learning; Parameter estimation for pairwise constrained samples...
  • Pegasos

  • Referenced in 94 articles [sw08752]
  • where λ is the regularization parameter of SVM. For a linear kernel, the total ... resulting algorithm is especially suited for learning from large datasets. Our approach also extends...
  • SMAC

  • Referenced in 59 articles [sw27215]
  • tool for optimizing algorithm parameters (or the parameters of some other process ... effective for the hyperparameter optimization of machine learning algorithms, scaling better to high dimensions ... tasks that are more scientifically valuable than parameter tuning...
  • FastInf

  • Referenced in 4 articles [sw06423]
  • well as representation and learning of shared parameters. It offers a rich interface that facilitates ... basic classes to other inference and learning methods...
  • ProPPR

  • Referenced in 1 article [sw32915]
  • probabilistic logic programming for structure discovery, parameter learning, and scalable inference. A key challenge ... framework that enables efficient learning and inference on graphs: using an abductive second-order probabilistic ... theories can be automatically generated via parameter learning; that in parameter learning, weight learning...
  • SparseFIS

  • Referenced in 10 articles [sw13736]
  • systems, extended by including rule weights. Our learning method consists of three phases: The first ... third phase estimates the linear consequent parameters by a regularized sparsity-constrained-optimization procedure ... local learning approach). Sparsity constraints are applied in order to force linear parameters...
  • DENSER

  • Referenced in 2 articles [sw34794]
  • network topology, but also tunes hyper-parameters (e.g., learning or data augmentation parameters). The automatic...