Japanese allows the numeral+classifier combination to appear in a variety of positions in relation to the head noun. This paper argues that it is necessary to posit at least four functional projections above NP (#P, CaseP, QP, and DP) and massive phrasal movement of such functional projections in order to provide a principled account for the structural diversity of the numeral+classifier combin...

The recognition of handwritten numeral is an important area of research for its applications in post office, banks and other organizations. This paper presents automatic recognition of handwritten Kannada numerals based on structural features. Five different types of features, namely, profile based 10-segment string, water reservoir; vertical and horizontal strokes, end points and average bound...

In this paper, we describe the system developed by the Australian National University (ANU), National ICT Australia (NICTA) for multimedia event detection applied to the TRECVID-2011 video retrieval benchmark. Our system uses five audio and visual features, leverages training events with cascaded classifier training, and sees performance improvements with spatial semantic features. A summary of...

We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule devel...

Selecting a relevant and discriminative combination of genes for cancer classification and building high-performing classifier are common and critical tasks in cancer classification problems. In this paper, a new approach is proposed to address the two issues at the same time. In details, BP neural network is employed to construct a classifier, and PSO algorithm is used to select a discriminati...

Today, handwriting recognition is one of the most challenging tasks and exciting areas of search in computer science. Indeed, despite the growing interest in this field, no satisfactory solution is available. For this reason Multiple Classifier Systems (MCS) based on the combination of outputs of a set of different classifiers have been proposed as a method for the developing of high performanc...

For combining classifiers at measurement level, the diverse outputs of classifiers should be transformed to uniform measures that represent the confidence of decision, hopefully, the class probability or likelihood. This paper presents our experimental results of classifier combination using confidence evaluation. We test three types of confidences: log-likelihood, exponential and sigmoid. For ...

This paper presents a Devnagari Numerical recognition method based on statistical discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes, image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance ar...

We decribe the submissions made by the National Research Council Canada to the Native Language Identification (NLI) shared task. Our submissions rely on a Support Vector Machine classifier, various feature spaces using a variety of lexical, spelling, and syntactic features, and on a simple model combination strategy relying on a majority vote between classifiers. Somewhat surprisingly, a classi...

The combination of classifiers is a powerful tool to improve the accuracy of classifiers, by using the prediction of multiple models and combining them. Many practical and useful combination techniques work by using the output of several classifiers as the input of a second layer classifier. The problem of this and other multi-classifier approaches is that huge amounts of memory are required to...