As text data becomes plentiful, unsupervised methods for Word Sense Disambiguation (WSD) become more viable. A problem encountered in applying WSD methods is finding the exact number of senses an ambiguity has in a training corpus collected in an automated manner. That number is not known a priori; rather it needs to be determined based on the data itself. We address that problem using cluster ...

Abstract Recent genomic studies have identified genes related to specific phenotypes. In addition to marginal association analysis for individual genes, analyzing gene pathways (functionally related sets of genes) may yield additional valuable insights. We have devised an approach to phenotype classification from gene expression profiling. Our method named “group Nearest Shrunken Centroids (gNS...

In this paper we propose a generalization to symbolic interval valued variables of the Principal Curves and Surfaces method proposed by T. Hastie in [4]. Given a data set X with n observations and m continuos variables the main idea of Principal Curves and Surfaces method is to generalize the principal component line, providing a smooth one-dimensional curved approximation to a set of data poin...

Varying coeecient models result from generalized linear models by allowing the parameter of the linear predictor to vary across some additional explanatory quantity called eeect modiier. While Hastie & Tibshirani (1993) have used spline smoothing techniques in varying-coeecient models with univariate response here the local likelihood approach is considered within the framework of multivariate ...

We propose a novel criterion for support vector machine learning: maximizing the margin in the input space, not in the feature (Hilbert) space. This criterion is a discriminative version of the principal curve proposed by Hastie et al. The criterion is appropriate in particular when the input space is already a well-designed feature space with rather small dimensionality. The definition of the ...

This paper describes an improved algorithm for the numerical solution to the Support Vector Machine (SVM) classification problem for all values of the regularization parameter, C. The algorithm is motivated by the work of Hastie et. al. and follows the main idea of tracking the optimality conditions of the SVM solution for descending value of C. It differs from Hastie’s approach in that the tra...

This paper describes an improved algorithm for the numerical solution to the Support Vector Machine (SVM) classification problem for all values of the regularization parameter, C. The algorithm is motivated by the work of Hastie et. al. and follows the main idea of tracking the optimality conditions of the SVM solution for descending value of C. It differs from Hastie’s approach in that the tra...

Nicholas L. Smith, PhD*; Jennifer E. Huffman, MSc*; David P. Strachan, MD*; Jie Huang, MD, MPH*; Abbas Dehghan, MD, PhD*; Stella Trompet, PhD*; Lorna M. Lopez, PhD*; So-Youn Shin, PhD*; Jens Baumert, PhD*; Veronique Vitart, PhD; Joshua C. Bis, PhD; Sarah H. Wild, MD, PhD; Ann Rumley, PhD; Qiong Yang, PhD; Andre G. Uitterlinden, PhD; David. J. Stott, MD, PhD; Gail Davies, PhD; Angela M. Carter, ...

There is significant literature which explores methods for clustering timeseries gene-expression data sets, such as the classical data set due to Spellman et al. (1998). For instance James and Hastie (2001) use linear or quadratic discriminant functions on fitted curves, while Bar-Joseph et al. (2003) using a similar approach, do the clustering based on the coefficients of the fitted splines. I...