
SVMTorch
 Referenced in 67 articles
[sw12121]
 Joachims [“Making largescale support vector machine learning practical”, in: B. Schölkopf, C. Burges ... algorithm, one can now efficiently solve largescale regression problems (more than 20000 examples). Comparisons ... largescale regression problems from G. Flake and S. Lawrence [Mach. Learn ... decomposition method for support vector machines (Tech. Rep.). National Taiwan University (2000)], we show that...

SHOGUN
 Referenced in 103 articles
[sw03517]
 machine learning toolbox, called SHOGUN, which is designed for unified largescale learning ... settings. It offers a considerable number of machine learning models such as support vector machines...

SimpleMKL
 Referenced in 67 articles
[sw12290]
 support vector machine, an efficient and general multiple kernel learning algorithm, based on semiinfinite ... tractable for largescale problems, by iteratively using existing support vector machine code. However ... that encourages sparse kernel combinations. Apart from learning the combination, we solve a standard...

LAMG
 Referenced in 34 articles
[sw06551]
 graphs arise in largescale computational applications such as semisupervised machine learning; spectral clustering...

MLlib
 Referenced in 25 articles
[sw15430]
 largescale data processing that is wellsuited for iterative machine learning tasks. In this...

SVMlight
 Referenced in 264 articles
[sw04076]
 implementation of Vapnik’s Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition ... regression, and for the problem of learning a ranking function. The optimization algorithms used ... algorithm for learning ranking functions [Joachims, 2002c]. The goal is to learn a function from ... version includes an algorithm for training largescale transductive SVMs. The algorithm proceeds by solving...

Spark
 Referenced in 40 articles
[sw23653]
 have been highly successful in implementing largescale dataintensive applications on commodity clusters. However ... parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis...

DryadLINQ
 Referenced in 7 articles
[sw23712]
 graph analysis, largescale log mining, and machine learning. We show that excellent absolute performance...

BindsNET
 Referenced in 1 article
[sw30217]
 neural networks, specifically geared towards machine learning and reinforcement learning. Our software, called BindsNET, enables ... enabling fast CPU and GPU computation for large spiking networks. The BindsNET framework ... spiking networks for largescale machine learning experimentation, and show some simple examples...

SGDLibrary
 Referenced in 6 articles
[sw26680]
 field of machine learning (ML). One promising approach for largescale data...

Nieme
 Referenced in 1 article
[sw14441]
 introduce Nieme, a machine learning library for largescale classification, regression and ranking. Nieme relies ... LeCun et al., 2006) which unifies several learning algorithms ranging from simple perceptrons to recent ... machine or l1regularized maximum entropy models. This framework also unifies batch and stochastic learning ... situations, but is particularly interesting for largescale learning tasks where both the examples...

LIBOL
 Referenced in 9 articles
[sw10887]
 stateoftheart online learning algorithms for largescale online classification tasks. We have ... users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform...

PNKHB
 Referenced in 2 articles
[sw40451]
 commonly the case in largescale parameter estimation, machine learning, and image processing. In each...

MFclass
 Referenced in 2 articles
[sw34570]
 accelerating the prediction of largescale computational models. Machine learning techniques typically rely on large...

LIBS2ML
 Referenced in 1 article
[sw28299]
 Order Machine Learning Algorithms. LIBS2ML is a library based on scalable second order learning algorithms ... solving largescale problems, i.e., big data problems in machine learning. LIBS2ML has been developed ... very slow and not suitable for largescale learning, or are in C/C++ which does ... provides machine learning practitioners an effective tool to deal with the largescale learning problems...

FASTCLIME
 Referenced in 12 articles
[sw10889]
 scalable and sophisticated tool for solving largescale linear programs. As an illustrative example ... useful to statisticians and machine learning researchers for solving a wide range of problems...

MLaut
 Referenced in 1 article
[sw27171]
 ecosystem. MLaut automates largescale evaluation and benchmarking of machine learning algorithms on a large ... provides a highlevel workflow interface to machine algorithm algorithms, implements a local back ... learning architecture. As a principal test case for MLaut, we conducted a largescale supervised ... performance of a number of machine learning algorithms  to our knowledge also the first larger...

ZOOpt
 Referenced in 2 articles
[sw22396]
 optimization problems in machine learning, addressing highdimensional, noisy, and largescale problems. The toolbox ... tools in realworld machine learning tasks...

LiblineaR
 Referenced in 5 articles
[sw25718]
 machine learning (available at ). ’LIBLINEAR’ is a simple library for solving large ... scale regularized linear classification and regression. It currently supports L2regularized classification (such as logistic...

DGLKE
 Referenced in 3 articles
[sw34088]
 Training Knowledge Graph Embeddings at Scale. Knowledge graphs (KGs) are data structures that store information ... approach of using KGs in various machine learning tasks is to compute knowledge graph embeddings ... scalable package for learning largescale knowledge graph embeddings. The package is implemented ... developers can run DGLKE on CPU machine, GPU machine, as well as clusters with...