An improved training algorithm for kernel Fisher discriminants

Acoustic Modelling Using Kernel-Based Discriminants

In this paper we use kernel-based Fisher Discriminants (KFD) for classification by integrating this method in a HMM-based speech recognition system. We translate the outputs of the KFD-classifier into conditional probabilities and use them as production probabilities of a HMM-based decoder for speech recognition. To obtain a good performance also in terms of computational complexity the Recursi...

Kernel Fisher discriminant for steganalysis of JPEG hiding methods

The use of kernel Fisher discriminants is used to detect the presence of JPEG based hiding methods. The feature vector for the kernel discriminant is constructed from the quantized DCT coefficient indices. Using methods developed in kernel theory a classifier is trained in a high dimensional feature space which is capable of discriminating original from stegoimages. The algorithm is tested on t...

Boosted Dyadic Kernel Discriminants

We introduce a novel learning algorithm for binary classification with hyperplane discriminants based on pairs of training points from opposite classes (dyadic hypercuts). This algorithm is further extended to nonlinear discriminants using kernel functions satisfying Mercer’s conditions. An ensemble of simple dyadic hypercuts is learned incrementally by means of a confidence-rated version of Ad...

An Improved Training Algorithm for Kernel Fisher Dis riminants

Kernel Fisher Discriminants

In this thesis we consider statistical learning problems and machines. A statistical learning machine tries to infer rules from a given set of examples such that it is able to make correct predictions on unseen examples. These predictions can for example be a classification or a regression. We consider the class of kernel based learning techniques. The main contributions of this work can be sum...