of the constrained model-space transform from the simple diagonal case to the full or block-diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time efficiency, and use for speaker adaptive training. The recognition performance of the two model-space

. 1. OPTIMAL ESTIMATION OF THE EIGENSPACE In this section, we show that the expected log-likelihood of the data is related to a sum of squared euclidean distances in the model space. This justifies using the SVD to compute the eigenspace. First, we will show that the log-likelihood of rows of MLLR

Perturbative scattering amplitudes in Yang-Mills theory have many unexpected properties, such as holomorphy of the maximally helicity violating amplitudes. To interpret these results, we Fourier transform the scattering amplitudes from momentum space to twistor space, and argue that the transformed

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper

of preserving privacy for the specified usage. In particular, we investigate the privacy transformation in the context of data mining applications like building classification and regression models. Second, our work improves on previous approaches by allowing more flexible generalizations for the data. Lastly

. This work is concerned with the numerical implementation of the space-transformation approach in porous media modeling. Space transformation is a mathematical approach that simplifies the study of multidimensional problems by reducing them to unidimensional ones. In particular, the implementation

We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a model. Our approach is based on the following observation: Any (PO)MDP can be transformed into an "

case of Besov spaces Bp;q, p <2, which model spatial inhomogeneity, is included. In comparison, linear procedures { SVD included { cannot attain optimal rates of convergence over such classes in the case p<2. For example, our methods achieve faster rates of convergence, for objects known to lie

Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult [43]. We present a hybrid neural network solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map

, as well as wavelet transform based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what