Results 231 to 240 of about 355,362 (266)

Multiple kernel learning

2020
The use of kernels in machine learning methods allows the identification of an optimal hyperplane for the separation of two classes (e.g., patients with a brain disorder of interest and healthy controls). When different acquisition modalities or different types of data are available, using a single kernel for all the available data is a disadvantage ...
Squarcina, Letizia, Castellani, Umberto, Brambilla, Paolo   +2 more
openaire   +2 more sources

Multiple Kernel Learning with Gaussianity Measures

Neural Computation, 2012
Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches.
Hideitsu Hino, Nima Reyhani, Noboru Murata   +2 more
openaire   +2 more sources

Soft Margin Multiple Kernel Learning

IEEE Transactions on Neural Networks and Learning Systems, 2013
Multiple kernel learning (MKL) has been proposed for kernel methods by learning the optimal kernel from a set of predefined base kernels. However, the traditional L1MKL method often achieves worse results than the simplest method using the average of base kernels (i.e., average kernel) in some practical applications.
Xinxing Xu, Ivor W. Tsang, Dong Xu 0001
openaire   +2 more sources

Multiple kernel extreme learning machine

Neurocomputing, 2015
Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms pay little attention to optimizing the choice of ...
Xinwang Liu, Lei  Wang, Guang-Bin Huang   +2 more
exaly   +3 more sources

Kernels for Generalized Multiple-Instance Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
The multiple-instance learning (MIL) model has been successful in numerous application areas. Recently, a generalization of this model and an algorithm for it were introduced, showing significant advantages over the conventional MIL model on certain application areas. Unfortunately, that algorithm is not scalable to high dimensions.
Qingping Tao, Stephen D. Scott 0001, N. V. Vinodchandran, Thomas Takeo Osugi, Brandon Mueller   +4 more
openaire   +2 more sources

Multiple kernel learning using composite kernel functions

Engineering Applications of Artificial Intelligence, 2017
Abstract Multiple Kernel Learning (MKL) algorithms deals with learning the optimal kernel from training data along with learning the function that generates the data. Generally in MKL, the optimal kernel is defined as a combination of kernels under consideration (base kernels). In this paper, we formulated MKL using composite kernel functions (MKLCKF)
Shiju S. S., Asif Salim, S. Sumitra 0001
openaire   +1 more source

Beta Process Multiple Kernel Learning

2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014
In kernel based learning, the kernel trick transforms the original representation of a feature instance into a vector of similarities with the training feature instances, known as kernel representation. However, feature instances are sometimes ambiguous and the kernel representation calculated based on them do not possess any discriminative information,
Bingbing Ni, Teng Li 0001, Pierre Moulin
openaire   +1 more source

Deep multilayer multiple kernel learning

Neural Computing and Applications, 2015
Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems.
Ilyes Rebai, Yassine Ben Ayed, Walid Mahdi   +2 more
openaire   +1 more source

Easy multiple kernel learning. [PDF]

, 2014
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a kernel based machine. In this paper, we propose a time and space ecient MKL algorithm that can easily cope with hundreds of thousands of kernels and more.
AIOLLI, FABIO, DONINI, MICHELE
openaire   +1 more source

Multiple kernel learning for image indexing

Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, 2010
The paper presents a novel framework for learning the hash functions for indexing through Multiple Kernel Learning. The Distance Based Hashing function is applied which does the object projection to hash space by preserving inter object distances. In recent works, the kernel matrix has been proved to be more accurate representation of similarity in ...
Ehtesham Hassan, Santanu Chaudhury, Madan Gopal   +2 more
openaire   +1 more source