This paper discusses the methodology and tools applied in the Parallel Grammar project (ParGram) to support consistency and parallelism of linguistic representations across multilingual Lexical Functional Grammar (LFG) grammars. A particular issue is that the grammars in the ParGram project are developed at different international sites. The approach that was established over several years reli...

The development of the Feature Space Mapping (FSM) system has been motivated by our conviction that neurodynamical models of the brain are very difficult, it is not clear how to model larger groups of neurons in a realistic way and simple neural networks are not well suited for cognitive modeling. Mind arises from a complex dynamics of the modular and hierarchical brain structures. Approximatio...

De novo protein structure prediction requires location of the lowest energy state of the polypeptide chain among a vast set of possible conformations. Powerful approaches include conformational space annealing, in which search progressively focuses on the most promising regions of conformational space, and genetic algorithms, in which features of the best conformations thus far identified are r...

This paper introduces two important issues of image registration. At first we want to recall the very general definition of mutual information that allows the choice of various feature spaces to perform image registration. Second we discuss the problem of finding the global maximum in an arbitrary feature space. We used a very general parallel, distributed memory, genetic optimization which tur...

A new feature space trajectory (FST) description of 3-D distorted views of an object is advanced for active vision applications. In an FST, di erent distorted object views are vertices in feature space. A new eigen-feature space and Fourier transform features are used. Vertices for di erent adjacent distorted views are connected by straight lines so that an FST is created as the viewpoint chang...

Feature Space Mapping (FSM) model is based on a network explicitly modeling probability distribution of the input/output data vectors. New theoretical developments of this model and results of applications to several classification problems are presented.

The article presents a method for both the unsupervised partitioning of a sample of data and the estimation of the possible number of inherent clusters which generate the data. This work exploits the notion that performing a nonlinear data transformation into some high dimensional feature space increases the probability of the linear separability of the patterns within the transformed space and...

Extreme learning machine (ELM), used for the “generalized” single-hidden-layer feedforward networks (SLFNs), is a unified learning platform that can use a widespread type of feature mappings. In theory, ELM can approximate any target continuous function and classify any disjoint regions; in application, many experiment results have already demonstrated the good performance of ELM. In view of th...

This paper presents a novel approach to the weak classifier selection based on the GentleBoost framework, based on sharing a set of features at each round. We explore the use of linear dimensionality reduction methods to guide the search for features that share some properties, such as correlations and discriminative properties. We add this feature set as a new parameter of the decision stump, ...