Add to ORKGIn this work, kernelized binary support vector machines are implemented based on stochastic gradient descent. The Scala library can be used both on a single computing node and on a Spark cluster. Additional tools for parameter tuning, subset selection, and model evaluation are implemented
We propose a distributed method to compute similarity (also known as kernel and Gram) matrices used ...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Learning from distributed data sets is common problem nowadays and the question of its actuality can...
We present a novel approach for training ker-nel Support Vector Machines, establish learn-ing runtim...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
Pervasive and networked computers have dramatically reduced the cost of collecting and distributing ...
SVMÉcole thématiqueKernel Machines is a term covering a large class of learning algorithms, includin...
© 2015 IEEE. We propose FS-Scala, a flexible and modular Scala based implementation of the Fixed Siz...
This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SM...
This thesis discusses the application of optimizations to machine learning algorithms. In particular...
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming...
Kernel Learning is widely used in pattern recognition and classification problems. We look at the be...
In the 90s, a new type of learning algorithm was developed, based on results from statistical learni...
The training of kernel support vector machine (SVM) is a computationally complex task for large data...
Kernel techniques became popular due to and along with the rising success of Support Vector Machines...
We propose a distributed method to compute similarity (also known as kernel and Gram) matrices used ...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Learning from distributed data sets is common problem nowadays and the question of its actuality can...
We present a novel approach for training ker-nel Support Vector Machines, establish learn-ing runtim...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
Pervasive and networked computers have dramatically reduced the cost of collecting and distributing ...
SVMÉcole thématiqueKernel Machines is a term covering a large class of learning algorithms, includin...
© 2015 IEEE. We propose FS-Scala, a flexible and modular Scala based implementation of the Fixed Siz...
This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SM...
This thesis discusses the application of optimizations to machine learning algorithms. In particular...
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming...
Kernel Learning is widely used in pattern recognition and classification problems. We look at the be...
In the 90s, a new type of learning algorithm was developed, based on results from statistical learni...
The training of kernel support vector machine (SVM) is a computationally complex task for large data...
Kernel techniques became popular due to and along with the rising success of Support Vector Machines...
We propose a distributed method to compute similarity (also known as kernel and Gram) matrices used ...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Learning from distributed data sets is common problem nowadays and the question of its actuality can...