A central problem in pattern recognition is the classi cation of a feature vector into one of several possible classes. In polychotomous classi cation the number of possible classes is higher than two. Given a set of training vectors with known classes, a classi er can be constructed using for example some density estimation or regression method. In a typical approach one estimates, using all t...

Behavioral forecasts have informed parole decisions in the United States since the 1920’s (Borden, 1928; Burgess, 1928). Over the decades, these forecasts have increasingly relied on quantitative methods that some would call actuarial (Messinger and Berk, 1987; Feely and Simon, 1994). Despite jurisprudential concerns and forecasting accuracy that has been di cult to evaluate (Farrington, 2003; ...

An assessment is made of Krook collisional models used to describe the kinetic behavior of collective oscillations, i.e., when Landau damping and collisions must be considered, as is often the case for low-frequency waves. The study focuses on an early energy-conserving model [B. D. Fried, A. N. Kaufman, and D. L. Sachs, Phys. Fluids 9, 292 (1966)] that is shown to be identical to a more modern...

In the literature, the nature of the relationships between memory processes and summary evaluations is still a debate. According to some theoretical approaches (e.g., “two-memory hypothesis”; Anderson, 1989) retrospective evaluations are based on the impression formed while attending to the to-beassessed stimuli (on-line judgment) – no functional dependence between information retrieval and jud...

The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principl...

This paper deals with statistical inferences based on the generalized varying-coeÆcient models proposed by Hastie and Tibshirani (1993). Local polynomial regression techniques are used to estimate coeÆcient functions and the asymptotic normality of the resulting estimators is established. The standard error formulas for estimated coeÆcients are derived and are empirically tested. A goodness-oft...

We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA ca...

Identification of ice floes and their outlines in satellite images is important for understanding physical processes in the polar regions, for transportation in ice-covered seas and for the design of offshore structures intended to survive in the presence of ice. At present this is done manually, a long and tedious process which precludes full use of the great volume of relevant images now avai...

A critical function of categories is their use in property inference (Heit, 2000). However, one challenge to using categories in inference is that most entities in the world belong to multiple categories (e.g., Fido could be a dog, a pet, a mammal, or a security system). Building on Patalano, Chin-Parker, and Ross (2006), we tested the hypothesis that category coherence (the extent to which cat...

The support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently due to its robustness and generalization capability. General theme here is to construct classifiers based on the training data in a high dimensional space by using all available dimensions. The SVM achieves huge data compression by selecting only few ob...