2014年6月20日 · Kernel minimum squared error (KMSE) is a simple and efficient learning algorithm extensively used by the machine learning community. The solution of KMSE is typically not sparse, which may be harmful for real time applications. Current proposed algorithms show hindrances when dealing with sample flow learning systems.

KMSE (88.7 FM) is a radio station licensed to Rochester, Minnesota. The station is owned by Minnesota Public Radio (MPR), and airs MPR's "The Current" network, consisting of an Adult Album Alternative music format originating from KCMP in Northfield, Minnesota .

2010年9月19日 · Abstract: In order to improve the feature extraction efficiency of KMSE, we propose a novel KMSE algorithm. This algorithm assumes that the discriminant vector can be expressed as the linear combination of “significant nodes”, a subset of the training samples rather than all training samples.

2015年12月1日 · Kernel Minimum Squared Error (KMSE) has become a hot topic in machine learning and pattern recognition in the past years. However, KMSE is essentially a binary classifier and one-against-all and one-against-one strategies are usually employed to deal with multi-class problems.

For continuous real output values, we find that KMSE is the kernel ridge regression (KRR) with a bias. Therefore KMSE can act as a general framework that includes KFD, LS-SVM and KRR as its particular cases. In addition, we simplify the formula to estimate the projecting direction of KFD.

In this paper we first show that the minimum squared-error solutions of kernel minimum squared-error (KMSE) models are neither unique nor numerically stable. We then propose a novel scheme for KMSE. This solution scheme can produce the unique solution and the …

2014年6月20日 · Kernel minimum squared error (KMSE) is a simple and efficient learning algorithm extensively used by the machine learning community. The solution of KMSE is typically not sparse, which may be harmful for real time applications.

2011年10月1日 · Among a lot of kernel methods, KMSE [4] is the kernelized version of minimum squared error (MSE) in kernel space. Compared with other kernel-based methods, KMSE has higher computational efficiency in the training phase.

2012年1月15日 · The kernel minimum squared error (KMSE) expresses the feature extractor as a linear combination of all the training samples in the high-dimensional kernel space. To extract a feature from a sample, KMSE should calculate as many kernel functions as the training samples.

In this paper, several methods which are developed for improving the classification efficiency of KMSE are assessed and their shortcomings are indicated. Then, KMSE is presented as a regression model. Taking advantage of local ridge regression, we develop an efficient KMSE classification technique.