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...

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...

3 How to utilize data more sufficiently is a crucial consideration in machine learning. Semi-supervised learning uses both unlabeled data and labeled data for this reason. However, Semi-Supervised Support Vector Machine (S3VM) focuses on maximizing margin only, and it abandons the instances which are not support vectors. This fact motivates us to modify maximum margin criterion to incorporate t...

Metric learning has been shown to significantly improve the accuracy of k-nearest neighbor (kNN) classification. In problems involving thousands of features, distance learning algorithms cannot be used due to overfitting and high computational complexity. In such cases, previous work has relied on a two-step solution: first apply dimensionality reduction methods to the data, and then learn a me...

Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are linear models, in this paper we investigate learning nonlinear tree models. ...