This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets texture classification. The core of such an is loss function that computes the distances between instances interest support vectors. objective to update weights CLs iteratively learn representation with large margin classes. Each iteration r...

Large margin linear classification methods have been successfully applied to many applications. For a linearly separable problem, it is known that under appropriate assumptions, the expected misclassification error of the computed “optimal hyperplane” approaches zero at a rate proportional to the inverse training sample size. This rate is usually characterized by the margin and the maximum norm...

We introduce a new family of positive-definite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed as mapping their inputs through a series of nonlinear feature spaces. These recursively derived k...

A fundamental question of machine learning is how to compare examples. If an algorithm could perfectly determine whether two examples were semantically similar or dissimilar, most subsequent machine learning tasks would become trivial (i.e, the 1-nearest-neighbor classifier will achieve perfect results). A common choice for a dissimilarity measurement is an uninformed norm, like the Euclidean d...

Large margin classifiers have demonstrated their advantages in many visual learning tasks, and have attracted much attention in vision and image processing communities. In this paper we apply and compare two large margin classifiers, Support Vector Machines and Sparse Network of Winnows, to detect faces in still gray scale images. Furthermore, we study the theoretical frameworks of these classi...

Label sequence learning is the problem of inferring a state sequence from an observation sequence, where the state sequence may encode a labeling, annotation or segmentation of the sequence. In this paper we give an overview of discriminative methods developed for this problem. Special emphasis is put on large margin methods by generalizing multiclass Support Vector Machines and AdaBoost to the...

In this paper, we present a supervised learning approach to training submodular scoring functions for extractive multi-document summarization. By taking a structured predicition approach, we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure. The learning method applies to all submodular summarization methods, and we demonstrate its effe...