- Referenced in 393 articles
- lasso  is an algorithm for learning the structure in an undirected Gaussian graphical model...
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- package bnlearn: Bayesian network structure learning, parameter learning and inference. Bayesian network structure learning, parameter ... learning and inference. This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC ... Search) and hybrid (MMHC and RSMAX2) structure learning algorithms for both discrete and Gaussian networks...
- Referenced in 69 articles
- exact and approximate inference, parameter and structure learning, and static and dynamic models...
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- well as algorithms for learning from hierarchically structured labels. In addition, it contains an evaluation...
- Referenced in 1176 articles
- Computation (PETSc) is a suite of data structures and routines that provide the building blocks ... users it initially has a much steeper learning curve than a simple subroutine library...
- Referenced in 87 articles
- orthogonality constraints. Such structured constraints appear pervasively in machine learning applications, including low-rank matrix...
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- data for design and analysis of structure learning algorithms. Background: The development of algorithms ... infer the structure of gene regulatory networks based on expression data is an important subject ... data sets that allow thorough testing of learning algorithms in a fast and reproducible manner...
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- Bayesian structure learning in sparse Gaussian graphical models. Decoding complex relationships among large numbers ... practice, sensible approach for structure learning. We illustrate the efficiency of the method...
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- Bayesian network structure learning by recursive autonomy identification. We propose the recursive autonomy identification ... constraint-based (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential ... this means and due to structure decomposition, learning a structure using RAI requires a smaller ... dimensionality. When the RAI algorithm learned structures from databases representing synthetic problems, known networks...
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- learning reliability method combining Kriging and Monte Carlo Simulation. An important challenge in structural reliability ... prediction which can be used in active learning methods. The aim of this paper ... structures in a more efficient way. The method is called AK-MCS for Active learning...
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- within the context of statistical learning theory and structural risk minimization. In the methods ... Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks and control are available. Robustness...
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- clause and solution-driven cube learning. By analyzing the structure of a formula, DepQBF tries...
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- predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping...
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- Graph visualization is a way of representing structural information as diagrams of abstract graphs ... software engineering, database and web design, machine learning, and in visual interfaces for other technical...
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- profile of available learning algorithms and corresponding optimizations. Its modular structure allows users to configure ... their own tailored learning scenarios, which exploit specific properties of their envisioned applications ... been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance ... magnitude, a necessary precondition to make automata learning applicable to realistic scenarios...
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- ADTs for several important machine learning structures, and various helper code ... tools to help you experiment with learning algorithms. See the Other Tools documentation heading ... tools for learning decision trees, for learning the structure belief nets (aka Bayesian networks ... much of it can scale to learning from very large data sets or from data...
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- Adaptation is facilitated through organizational structural change and two learning mechanisms: learning from past experiences...
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- express various aspects of model structure, inference, and learning. By combining the traditional, declarative, statistical...
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- methods allow the analysis of the structures learned by the probabilistic models, the visualization...
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- MALSAR (Multi-tAsk Learning via StructurAl Regularization) package includes the following multi-task learning algorithms ... Learning; Trace-Norm Regularized Multi-Task Learning; Alternating Structural Optimization; Incoherent Low-Rank and Sparse ... Multi-Task Learning; Multi-Task Learning with Graph Structures; Disease Progression Models; Incomplete Multi-Source...