Add to ORKGAbstract RLScore is a Python open source module for kernel based machine learning. The library provides implementations of several regularized least-squares (RLS) type of learners. RLS methods for regression and classification, ranking, greedy feature selection, multi-task and zero-shot learning, and unsupervised classification are included. Matrix algebra based computational short-cuts are used to ensure efficiency of both training and cross-validation. A simple API and extensive tutorials allow for easy use of RLScore
Support vector machines (SVMs) and regularized least squares (RLS) are two recent promising techniqu...
In this paper, we revisited the classical technique of Regularized Least Squares (RLS) for the class...
The kernel regularized least squares (KRLS) method uses the kernel trick to perform non-linear regre...
RLScore is a Python open source module for kernel based machine learning. The library provides imple...
We propose a novel algorithm for greedy forward fea-ture selection for regularized least-squares (RL...
Kernel-based regularized least squares (RLS) algorithms are a promising technique for classification...
We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm...
This is a collection of information about regularized least squares (RLS). The facts here are not ne...
AbstractWe survey a number of recent results concerning the behaviour of algorithms for learning cla...
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised...
We revisit the classical technique of regularised least squares (RLS) for nonlinear classification i...
Supervised learning with pair-input data has recently become one of the most intensively studied ...
We propose an efficient algorithm for calculating hold-out and cross-validation (CV) type of estimat...
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Compute...
In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost fun...
Support vector machines (SVMs) and regularized least squares (RLS) are two recent promising techniqu...
In this paper, we revisited the classical technique of Regularized Least Squares (RLS) for the class...
The kernel regularized least squares (KRLS) method uses the kernel trick to perform non-linear regre...
RLScore is a Python open source module for kernel based machine learning. The library provides imple...
We propose a novel algorithm for greedy forward fea-ture selection for regularized least-squares (RL...
Kernel-based regularized least squares (RLS) algorithms are a promising technique for classification...
We present GURLS, a toolbox for supervised learning based on the regularized least squares algorithm...
This is a collection of information about regularized least squares (RLS). The facts here are not ne...
AbstractWe survey a number of recent results concerning the behaviour of algorithms for learning cla...
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised...
We revisit the classical technique of regularised least squares (RLS) for nonlinear classification i...
Supervised learning with pair-input data has recently become one of the most intensively studied ...
We propose an efficient algorithm for calculating hold-out and cross-validation (CV) type of estimat...
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Compute...
In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost fun...
Support vector machines (SVMs) and regularized least squares (RLS) are two recent promising techniqu...
In this paper, we revisited the classical technique of Regularized Least Squares (RLS) for the class...
The kernel regularized least squares (KRLS) method uses the kernel trick to perform non-linear regre...