The authors describe a feature selection technique suitable for right censored survival data. They transform the original regression problem (prediction of the time of an event) into a binary classification problem by stratifying patients into a low-risk group (time < 5 years) and a highrisk group (time >= 5 years). First, they reduce the dimensionality of the data by employing factor analysis....

The R package gset calculates equivalence and futility boundaries based on the exact bivariate non-central t test statistics. It is the first R package that targets specifically at the group sequential test of equivalence hypotheses. The exact test approach adopted by gset neither assumes the large-sample normality of the test statistics nor ignores the contribution to the overall Type I error ...

In the standard feature selection problem, we are given a fixed set of candidate features for use in a learning problem, and must select a subset that will be used to train a model that is “as good as possible” according to some criterion. In this paper, we present an interesting and useful variant, the online feature selection problem, in which, instead of all features being available from the...

Bidirectional texture functions (BTFs) represent the appearance of complex materials. Three major shortcomings with BTFs are the bulky storage, the difficulty in editing and the lack of efficient rendering methods. To reduce storage, many compression techniques have been applied to BTFs, but the results are difficult to edit. To facilitate editing, analytical models have been fit, but at the co...

In this chapter, we will study algorithms for both two-stage as well as multi-stage stochastic mixed-integer programs. We present stagewise (resourcedirective) decomposition methods for two-stage models, and scenario (pricedirective) decomposition methods for multi-stage models. The manner in which these models are decomposed relies not only on the specific data elements that are random, but al...

Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet...

The hard support vector regression (HSVR) usually has a risk of suffering from overfitting due to the presence of noise. The main reason is that it does not utilize the regularization technique to set an upper bound on the Lagrange multipliers so they can be magnified infinitely. Hence, we propose a greedy stagewise based algorithm to approximately train HSVR. At each iteration, the sample whic...

We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both semisupervised problems and unsupervised problems. The augmented cost is minimized in a greedy, stagewise functional minimization procedure as in GradientBoost. Our method provides insights into generalization issues in...

The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A fitting procedure is derived which works for all simple exponential family distributions, including bin...