We present a maximum margin framework that clusters data using latent variables. Using latent representations enables our framework to model unobserved information embedded in the data. We implement our idea by large margin learning, and develop an alternating descent algorithm to effectively solve the resultant non-convex optimization problem. We instantiate our latent maximum margin clusterin...

In the treatment of cancer the fundamental surgical goal is to remove all local malignant disease and leave no residual malignant cells. Studies have demonstrated the benefit of achieving negative resection margins in terms of disease free local recurrence and overall survival. The surgical margins for Head & Neck cancer may vary widely depending on the site of disease. This variation reflects ...

Most object detection methods operate by applying a binary classifier to sub-windows of an image, followed by a non-maximum suppression step where detections on overlapping sub-windows are removed. Since the number of possible sub-windows in even moderately sized image datasets is extremely large, the classifier is typically learned from only a subset of the windows. This avoids the computation...

Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it facilitates generalization error analysis, and practically interesting because it presents a clear geometric interpretation of the models being built. We formulate and prove a suf£cient condition fo...

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...

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...