• Neural Network Toolbox

  • Referenced in 175 articles [sw07378]
  • dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With...
  • Scikit

  • Referenced in 365 articles [sw08058]
  • machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing...
  • KEEL

  • Referenced in 103 articles [sw06791]
  • various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based...
  • GloVe

  • Referenced in 50 articles [sw26211]
  • Word Representation. GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training...
  • LS-SVMlab

  • Referenced in 26 articles [sw07367]
  • been introduced within the context of statistical learning theory and structural risk minimization ... Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks and control are available. Robustness...
  • Statistics Toolbox

  • Referenced in 18 articles [sw10157]
  • Statistics Toolbox™ provides statistical and machine learning algorithms and tools for organizing, analyzing, and modeling ... regression. The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs...
  • mdp

  • Referenced in 9 articles [sw14129]
  • collection of supervised and unsupervised learning algorithms and other data processing units that...
  • RFCM

  • Referenced in 7 articles [sw02668]
  • rough and fuzzy sets. A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means...
  • RolX

  • Referenced in 5 articles [sw32343]
  • linear in the number of edges), unsupervised learning approach for automatically extracting structural roles from...
  • CHIME

  • Referenced in 5 articles [sw28514]
  • with EM algorithm and its optimality. Unsupervised learning is an important problem in statistics...
  • quanteda

  • Referenced in 6 articles [sw30853]
  • applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses...
  • GLISSOM

  • Referenced in 6 articles [sw04192]
  • subsequently organized through an unsupervised Hebbian learning process using visual input. Weak connections are eliminated...
  • subgraph2vec

  • Referenced in 3 articles [sw36496]
  • neighbourhoods of nodes to learn their latent representations in an unsupervised fashion. We demonstrate that ... could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel ... graph kernels on both supervised and unsupervised learning tasks. Specifically, on two realworld program analysis...
  • FoldingNet

  • Referenced in 3 articles [sw32562]
  • been state-of-the-art for supervised learning tasks on point clouds such as classification ... auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder...
  • InfoGraph

  • Referenced in 2 articles [sw37754]
  • InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. This paper ... studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph ... representatives. Inspired by recent progress of unsupervised representation learning, in this paper we proposed ... maximizes the mutual information between unsupervised graph representations learned by InfoGraph and the representations learned...
  • PixelVAE

  • Referenced in 2 articles [sw36214]
  • modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent...
  • TimeSeriesClustering

  • Referenced in 2 articles [sw34951]
  • TimeSeriesClustering is a Julia implementation of unsupervised learning methods for time series datasets. It provides...
  • GMCM

  • Referenced in 2 articles [sw24057]
  • mixture copula models (GMCM) for general unsupervised learning based on clustering. Li, Brown, Huang...
  • CLUSTER3

  • Referenced in 2 articles [sw03489]
  • Conceptual clustering is a form of unsupervised learning that seeks clusters in data that represent...
  • SOFAR

  • Referenced in 4 articles [sw31665]
  • with broad applications to both unsupervised and supervised learning tasks, such as biclustering with sparse...