Mathematical software

Spider
Referenced in 8 articles [sw10713]
spider - machine learning toolbox for Matlab. It’s a library of objects in Matlab ... reasonably) large unsupervised, supervised or semi-supervised machine learning problems. Aims to become a complete...

DIFFRAC
Referenced in 4 articles [sw23902]
state-of-the-art performance for semi-supervised learning, for clustering or classification. We present...

RALF
Referenced in 2 articles [sw33955]
part of the project Semi-supervised learning in image collections. This framework combines active learning...

BlinkFill
Referenced in 2 articles [sw29485]
large space. We present a semi-supervised learning technique to significantly reduce this ambiguity...

pomegranate
Referenced in 1 article [sw26684]
core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider...

GBFlearn
Referenced in 1 article [sw36067]
GBFlearn - Learning with Graph Basis Functions. A very simple toolbox to illustrate how graph basis ... used for interpolation, classification and semi-supervised learning on graphs...

GraphDemo
Referenced in 1 article [sw10148]
their use in machine learning. Many machine learning algorithms model local neighborhoods using similarity graphs ... denoising, spectral clustering, label propagation for semi-supervised learning, and so on. However, for most...

CLEMM
Referenced in 1 article [sw31323]
learning technique in multivariate statistics and machine learning. In this paper, we propose ... envelope methods in unsupervised and semi-supervised learning problems. Numerical studies on simulated data...

MEKA
Referenced in 12 articles [sw15429]
variety of methods for this type of learning. We present MEKA: an open-source Java ... target data, including in incremental and semi-supervised contexts...

Manifold Regularization
Referenced in 1 article [sw24840]
marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data ... handle both transductive and truly semi-supervised settings. We present experimental evidence suggesting that ... semi-supervised algorithms are able to use unlabeled data effectively. Finally we have a brief ... discussion of unsupervised and fully supervised learning within our general framework...

FRaC
Referenced in 3 articles [sw15627]
verified “normal” data (semi-supervised anomaly detection). Traditional approaches typically compare the position ... appear in anomalies. Our approach is to learn predictive models of the relationships among...

TopicRNN
Referenced in 2 articles [sw36211]
contextual RNN language modeling, our model is learned end-to-end. Empirical results on word ... resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making...

Spider