There has been an increasing research interest in natural language call routing (NLCR) applications. One of the challenges often encountered in NLCR applications is the difficulty of performing language-dependent tasks such as morphological analysis of words and stop-word filtering. In this paper, we propose a novel NLCR system which does not depend on languagespecific information and thus, it ...

This paper proposes a novel control scheme for channel equalization for wireless communication system. The proposed scheme considers channel equalization as a classification problem. For efficient solution of the problem, this paper makes use of a neural network working on Algebraic Perceptron (AP) algorithm as a classifier. Also, this paper introduces a method of performance improvement by inc...

Applications of multi-class classification, such as document categorization, often appear in cost-sensitive settings. Recent work has significantly improved the state of the art by moving beyond “flat” classification through incorporation of class hierarchies [4]. We present a novel algorithm that goes beyond hierarchical classification and estimates the latent semantic space that underlies the...

The importance of classification algorithms in the context of risk assessment is briefly explained. As an alternative to the popular support vector machines fault tolerant perceptron learning is suggested. In order to achieve better generalization properties the additional use of an iterative large margin perceptron algorithm is investigated. In particular it is shown that care has to be taken ...

In hierarchical classification, class label is structured in that each label value corresponds to one non-root node in a tree, where the inter-class relationship for classification is specified by directed paths of the tree. In such a situation, the focus has been on how to leverage the inter-class relationship to enhance the performance of flat classification ignoring such dependency. This is ...

Viewing machine translation as a structured classification problem has provided a gateway for a host of structured prediction techniques to enter the field. In particular, large-margin structured prediction methods for discriminative training of feature weights, such as the structured perceptron or MIRA, have started to match or exceed the performance of existing methods such as MERT. One issue...

Automatic speech recognition has long been a considered dream. While ASR does work today, and it is commercially available, it is extremely sensitive to noise, talker variations, and environments. The current state-of-the-art automatic speech recognizers are based on generative models that capture some temporal dependencies such as hidden Markov models (HMMs). While HMMs have been immensely imp...

In the area of data classification, one of the prominent algorithms is the large margin nearest neighbor (LMNN) approach which is a metric learning to enhance the performance of the popular k-nearest neighbor classifier. In principles, LMNN learns a more efficient metric in the input space by using a linear mapping as the outcome of a convex optimization problem. However, one of the greatest we...

A method is introduced to learn and represent similarity with linear operators in kernel induced Hilbert spaces. Transferring error bounds for vector valued large-margin classifiers to the setting of Hilbert-Schmidt operators leads to dimension free bounds on a risk functional for linear representations and motivates a regularized objective functional. Minimization of this objective is effected...

In the present paper, a novel image classification method that uses the hierarchical structure of categories to produce more semantic prediction is presented. This implies that our algorithm may not yield a correct prediction, but the result is likely to be semantically close to the right category. Therefore, the proposed method is able to provide a more informative classification result. The m...