26 search results - page 1 / 6 » Random Feature Maps for Dot Product Kernels |
CORR
2012
Springer
12 years 9 days ago
2012
Springer
Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and...
NIPS
2007
13 years 6 months ago
2007
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
ACCV
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in...
GECCO
2005
Springer
13 years 10 months ago
2005
Springer
In support vector machines (SVM), the kernel functions which compute dot product in feature space significantly affect the performance of classifiers. Each kernel function is suit...
NECO
1998
13 years 4 months ago
1998
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...