Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bound for the generalization error of any voting classifier in terms of the margin distribution over the training data. However, Breiman raised important questions about the margin explanation by developing a boosting alg...

BACKGROUND Knowledge of prognostic factors following resection of rectal cancer may be used in the selection of patients for adjuvant therapy. This study examined the prognostic impact of the circumferential resection margin on local recurrence, distant metastasis and survival rates. METHODS A national population-based rectal cancer registry included all 3319 new patients from November 1993 t...

In this study we describe efforts to use machine learning to out-perform the expert Las Vegas line-makers at predicting the outcome of NFL football games. The statistical model we employ for inference is the Gaussian process, a powerful tool for supervised learning applications. With predictions for the margin of victory and associated confidence intervals from the Gaussian process model, we pr...

A contribution to a special issue on Hormones and Human Competition. Social competition is associated with marked emotional, behavioral and hormonal responses, including changes in testosterone levels. The strength and direction of these responses is often modulated by levels of other hormones (e.g. cortisol) and depends on psychological factors - classically, the objective outcome of a competi...

it is very important to analyze the rice market structure in mazandaran province, as this province is competent to produce rice. mazandaran rice market was analyzed by completing 55 questionnaires in producer, wholesaler and retailers level, randomly in 2009. results show that marketing margins of two varieties namely- local (tarom) and multi-product- were 5850 and 3700 rials, respectively; als...

where σ1, ...σn are iid Rademacher random variables. Rn(F ) characterizes the extent to which the functions in F can be best correlated with a Rademacher noise sequence. A number of generalization error bounds have been proposed based on Rademacher complexity [1,2]. In this open problem, we introduce a new complexity measure for function classes. We focus on function classes F that is the conve...

We consider the problem of learning Bayesian network classifiers that maximize the margin over a set of classification variables. We find that this problem is harder for Bayesian networks than for undirected graphical models like maximum margin Markov networks. The main difficulty is that the parameters in a Bayesian network must satisfy additional normalization constraints that an undirected g...

We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations. The approach is inspired by, and has strong connections to, large-margin linear discrimination. We show how to learn low-norm factorizations by solving a semi-definite program, and discuss generalization error bounds for them.

Code recommender systems ease the use and learning of software frameworks and libraries by recommending calls based on already present code. Typically, code recommender tools have been based on rather simple rule based systems. On the other hand recent advances in Recommender Systems and Collaborative Filtering have been mainly focused on rating data. While many of these advances can be incorpo...

Recent theoretical results have shown that improved bounds on generalization error of classifiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make use of the margin distribution and are driven by optimization with respect to the points that are closest to the hyperplane. This paper enhance...