Based on the Confidence Distribution method to the Behrens-Fisher problem, we consider two approaches of combining Confidence Distributions: P Combination and AN Combination to solve the Behrens-Fisher problem. Firstly, we provide some Confidence Distributions to the BehrensFisher problem, and then we give the Confidence Distribution method to the Behrens-Fisher problem. Finally, we compare the...

Jaakkola and Haussler (1999a) introduced the Fisher kernel (named in honour of Sir Ronald Fisher), thus creating a generic mechanism for incorporating generative probability models into discriminative classifiers such as SVMs. Jaakkola and Haussler (1999b) introduced a generic class of probabilistic regression models and a parameter estimation technique that can make use of arbitrary kernel fun...

Few years back, Jaakkola and Haussler published a method of combining generative and discriminative approaches for detecting protein homologies. The method was a variant of support vector machines using a new kernel function called Fisher Kernel. They begin by training a generative hidden Markov model for a protein family. Then, using the model, they derive a vector of features called Fisher sc...

3Thermo Fisher Scientifi c, Sunnyvale, CA P o ster N o te 2 14 2 A Sensitive Method for Direct Analysis of Impurities in Apramycin and Other Aminoglycoside Antibiotics Using Charged Aerosol Detection Zhen Long1, Qi Zhang2, an J n1, Lina L ang1, Bruce B iley2, Ian Acworth2, Deepali Mohindra3 1Thermo Fisher Scientific, Shanghai, China, 2Thermo Fisher Scientific, Chelmsford, MA, USA and 3Thermo Fi...

This paper proposes a new method of feature extraction and recognition, namely, the fuzzy inverse Fisher discriminant analysis (FIFDA) based on the inverse Fisher discriminant criterion and fuzzy set theory. In the proposed method, a membership degree matrix is calculated using FKNN, then the membership degree is incorporated into the definition of the between-class scatter matrix and withinExp...

Fisher criterion has achieved great success in dimensionality reduction. Two representative methods based on Fisher criterion are Fisher Score and Linear Discriminant Analysis (LDA). The former is developed for feature selection while the latter is designed for subspace learning. In the past decade, these two approaches are often studied independently. In this paper, based on the observation th...

Fisher linear discriminant analysis (LDA) can be sensitive to the problem data. Robust Fisher LDA can systematically alleviate the sensitivity problem by explicitly incorporating a model of data uncertainty in a classification problem and optimizing for the worst-case scenario under this model. The main contribution of this paper is show that with general convex uncertainty models on the proble...

The bimodality of a population P can be measured by dividing its range into two intervals so as to maximize the Fisher distance between the resulting two subpopulations P1 and P2. If P is a mixture of two (approximately) Gaussian subpopulations, then P1 and P2 are good approximations to the original Gaussians, if their Fisher distance if great enough. For a histogram having n bins this method o...