This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters ...

Particle classification is an important component of multivariate statistical analysis methods that has been used extensively to extract information from electron micrographs of single particles. Here we describe a new Bayesian Gibbs sampling algorithm for the classification of such images. This algorithm, which is applied after dimension reduction by correspondence analysis or by principal com...

We report on an empirical comparison of several multivariate classification techniques (e.g., random forests, Bayesian classification, support vector machines) for signal identification; our experiments use K* mass as a test case. We show 1) the effect of using different cost matrices in generalization performance and 2) how information about physical constraints obtained from kinematic fitting...

Mixtures of distributions have been used as probability models for clustering data. Classification maximum likelihood (CML) procedure is a popular mixture of maximum likelihood approach to clustering. Yang (1993) extended CML to fuzzy CML (FCML) for a normal mixture model, called FCML-N. However, normal distributions are not robust for outliers. In general, t-distributions should be more robust...

Multivariate Public Key Cryptography(MPKC) has become one of a few options for security in the quantum model of computing. Though a few multivariate systems have resisted years of effort from the cryptanalytic community, many such systems have fallen to a surprisingly small pool of techniques. There have been several recent attempts at formalizing more robust security arguments in this venue wi...

Introduction: Central nervous system factors are now understood to be important in the etiology of temporomandibular disorders (TMD), but knowledge concerning objective markers of central pathophysiology in TMD is lacking. Multivariate analysis techniques like support vectormachines (SVMs) could generate important discoveries regarding the expression of pain centralization in TMD. Support vecto...

Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of function...

A novel class of models for multivariate time series is presented. We consider hierarchical mixture-of-expert (HME) models in which the experts, or building blocks of the model, are vector autoregressions (VAR). It is assumed that the VAR-HME model partitions the covariate space, specifically including time as a covariate, into overlapping regions called overlays. In each overlay a given number...