We present an approximate policy iteration algorithm that uses rollouts to estimate the value of each action under a given policy in a subset of states and a classifier to generalize and learn the improved policy over the entire state space. Using a multiclass support vector machine as the classifier, we obtained successful results on the inverted pendulum and the bicycle balancing and riding d...

In this paper, we construct a learning method of stochastic perceptron based on semiparametric inference, and show that this method produces large margin solutions. In semiparametric inference, the parameters are divided into structural parameters which are to be estimated and nuisance parameters in which we do not have any interest. Here, the weight vector of perceptron is de ned as structural...

We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an -class problem, the DDAG contains classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on and on the margin ...

Abstract: Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership of one of two classes. In the literature, there exists a distinction between hard and soft classification. In soft classification, the conditional class probability is modeled as a function of the covariates. In contrast, hard c...

Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task. We present a novel approach based on large margin learning that combines kernel based splice site predictions with common sequence alignment techniques. By solving a conve...

We propose a framework for dealing with binary hard-margin classification in Banach spaces, centering on the use of a supporting semi-inner-product (s.i.p.) taking the place of an inner-product in Hilbert spaces. The theory of semi-inner-product spaces allows for a geometric, Hilbert-like formulation of the problems, and we show that a surprising number of results from the Euclidean case can be...

In this paper, we propose to use a new optimization method, i.e., semidefinite programming (SDP), to solve large margin estimation (LME) problem of continuous density hidden Markov models (CDHMM) for speech recognition. First of all, we introduce a new constraint into the LME to guarantee the boundedness of the margin of CDHMM. Secondly, we show that the LME problem under this new constraint ca...

Monotonic classification plays an important role in the field of decision analysis, where decision values are ordered and the samples with better feature values should not be classified into a worse class. The monotonic classification tasks seem conceptually simple, but difficult to utilize and explain the order structure in practice. In this work, we discuss the issue of feature selection unde...

Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations. One of the most prominent multi-task learning algorithms is an extension to support vector machines (svm) by Evgeniou et al. [15]. Although very elegant, multi-task svm is inherently restricted by the fact that support vector machines ...

In this paper, we consider unsupervised partitioning problems, such as clustering, image segmentation, video segmentation and other change-point detection problems. We focus on partitioning problems based explicitly or implicitly on the minimization of Euclidean distortions, which include mean-based change-point detection, K-means, spectral clustering and normalized cuts. Our main goal is to le...