Professors Candès and Tao are to be congratulated for their innovative and valuable contribution to high-dimensional sparse recovery and model selection. The analysis of vast data sets now commonly arising in scientific investigations poses many statistical challenges not present in smaller scale studies. Many of these data sets exhibit sparsity where most of the data corresponds to noise and o...

Multi-class pattern recognition has a wide range of applications including handwritten digit recognition (Chiang, 1998), speech tagging and recognition (Athanaselis, Bakamidis, Dologlou, Cowie, Douglas-Cowie & Cox, 2005), bioinformatics (Mahony, Benos, Smith & Golden, 2006) and text categorization (Massey, 2003). This chapter presents a comprehensive and competitive study in multi-class neural ...

The two major protease inhibitors in mouse plasma are alpha 1-protease inhibitor (alpha 1-PI), putative inhibitor of neutrophil elastase, and contrapsin, an inhibitor in vitro of trypsinlike proteases. We have shown by nucleotide sequence analysis that these two inhibitors are related (R. E. Hill, P. H. Shaw, P. A. Boyd, H. Baumann, and N. D. Hastie, Nature (London) 311:175-177, 1984). Here, we...

In recent years, stereotype theorizing has been dominated by the social cognitive approach (Park & Hastie, 1987; Schneider, 1991). This viewpoint has emphasized the importance of social categorization to the process of stereotyping and its researchers have attempted to understand not only the antecedents and consequences of categorization but also the link between categorization and stereotypin...

The Support Vector Machine (SVM) has become one of the most popular machine learning techniques in recent years. The success of the SVM is mostly due to its elegant margin concept and theory in binary classification. Generalization to the multicategory setting, however, is not trivial. There are a number of different multicategory extensions of the SVM in the literature. In this paper, we revie...

Varying-coefficient models with categorical effect modifiers are considered within the framework of generalized linear models. We distinguish between nominal and ordinal effect modifiers, and propose adequate Lasso-type regularization techniques that allow for (1) selection of relevant covariates, and (2) identification of coefficient functions that are actually varying with the level of a pote...

In classification, with an increasing number of variables, the required number of observations grows drastically. In this paper we present an approach to put into effect the maximal possible variable selection, by splitting a K class classification problem into pairwise problems. The principle makes use of the possibility that a variable that discriminates two classes will not necessarily do so...

If one could predict which of two classifiers will correctly classify a particular sample, then one could use the better classifier. Continuing this selection process throughout the data set should result in improved accuracy over either classifier alone. Fortunately, scalar measures which relate to the degree of confidence that we have in a classification can be computed for most common classi...

Most of this article concerns the uses of LARS and the two related methods in the age-old, " somewhat notorious, " problem of " [a]utomatic model-building algorithms.. . " for linear regression. In the following, I will confine my comments to this notorious problem and to the use of LARS and its relatives to solve it. 1. The implicit assumption. Suppose the response is y, and we collect the m p...

Previous researchers developed new learning architectures for sequential data by extending conventional hidden Markov models through the use of distributed state representations. Although exact inference and parameter estimation in these architectures is computationally intractable, Ghahramani and Jordan (1997) showed that approximate inference and parameter estimation in one such architecture,...