We present a method to simultaneously learn a mixture of mappings and large margin hyperplane classifier. This method learns useful mappings of the training data to improve classification accuracy. We first present a simple iterative algorithm that finds a greedy local solution and then derive a semidefinite relaxation to find an approximate global solution. This relaxation leads to the matrix ...

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 present generalization error bounds based on analyzing the Rademacher complexity of low-norm factorizations.

the biologic width (bw) includes attached epithelial cells and connective tissue attachment complex being very important in the periodontal health during prosthetic treatments as invading this zone can cause bone resorption and gingival recession. the present study investigated biologic width values in the normal periodontium in anterior and posterior teeth. 30 patients that referred from resto...

the study specifically described the socio-economic status of the people involved in the production, distribution and consumption of convenience foods in lafia urban of nasarawa state. it identified the factors that influence the entry into convenience food enterprise, factors necessitating the demand and supply of the products and examined the costs and returns of three convenience foods. the ...

Rectal cancer carries poor prognosis because of metastasis and local recurrence. Local recurrence has a profound effect on morbidity and quality of life. Therefore, preoperative staging of rectal cancer has an important impact on treatment plan. The main factor in predicting the local recurrence is the circumferential resection margin (CRM). Surgical resection with stage-appropriate neoadjuvant...

Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. Recent theoretical results, however, disclosed that maximizing the minimum margin does not necessarily lead to better generalization performances, and instead, the margin distribu...

We present a hierarchical maximum-margin clustering method for unsupervised data analysis. Our method extends beyond flat maximummargin clustering, and performs clustering recursively in a top-down manner. We propose an effective greedy splitting criteria for selecting which cluster to split next, and employ regularizers that enforce feature sharing/competition for capturing data semantics. Exp...

We introduce Gaussian Margin Machines (GMMs), which maintain a Gaussian distribution over weight vectors for binary classification. The learning algorithm for these machines seeks the least informative distribution that will classify the training data correctly with high probability. One formulation can be expressed as a convex constrained optimization problem whose solution can be represented ...

In this paper, we propose a maximum-margin framework for classification using Nonnegative Matrix Factorization. In contrast to previous approaches where the classification and matrix factorization stages are separated, we incorporate the maximum margin constraints within the NMF formulation, i.e we solve for a base matrix that maximizes the margin of the classifier in the low dimensional featur...

In this paper we study output coding for multi-label prediction. For a multi-label output coding to be discriminative, it is important that codewords for different label vectors are significantly different from each other. In the meantime, unlike in traditional coding theory, codewords in output coding are to be predicted from the input, so it is also critical to have a predictable label encodi...