This paper proposes a Multiclass Least Squares Twin Support Vector Machine (MLSTSVM) classifier for multi-class classification problems. The formulation of MLSTSVM is obtained by extending the formulation of recently proposed binary Least Squares Twin Support Vector Machine (LSTSVM) classifier. For M-class classification problem, the proposed classifier seeks M-non parallel hyper-planes, one fo...

This paper proposes the use of feature weights to reveal the hierarchical nature of music audio. Feature weighting has been exploited in machine learning, but has not been applied to music audio segmentation. We describe both a global and a local approach to automatic feature weighting. The global approach assigns a single weighting to all features in a song. The local approach uses the local s...

Rapid prototyping (RP) methods are used for production easily and quickly of a scale model of a physical part or assembly. Gas metal arc welding (GMAW) is a widespread process used for rapid prototyping of metallic parts. In this process, in order to obtain a desired welding geometry, it is very important to predict the weld bead geometry based on the input process parameters, which are voltage...

A new approach to short-term electrical load forecasting is investigated in this paper. As electrical load data are highly non-linear in nature, in the proposed approach, we first separate out the linear and the non-linear parts, and then forecast using the non-linear part only. Semi-parametric spectral estimation method is used to decompose a load data signal into a harmonic linear signal mode...

Background: External beam radiotherapy and brachytherapy plays a vital role in the management of cancer cervix.  High dose rate brachytherapy is being presently used worldwide for the brachytherapy applications. At present, 2-Dimensional linear array detectors   are the most common QA tool used for pretreatment patient specific quality assurance in external beam radiotherapy alon...

Current state-of-the-art Statistical Machine Translation systems are based on log-linear models that combine a set of feature functions to score translation hypotheses during decoding. The models are parametrized by a vector of weights usually optimized on a set of sentences and their reference translations, called development data. In this paper, we explore a (common and industry relevant) sce...

We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly a...

Most state-of-the-art statistical machine translation systems use log-linear models, which are defined in terms of hypothesis features and weights for those features. It is standard to tune the feature weights in order to maximize a translation quality metric, using heldout test sentences and their corresponding reference translations. However, obtaining reference translations is expensive. In ...

Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, wh...

Word reordering is one of the fundamental problems of machine translation, and an important factor of its quality and efficiency. In this paper, we introduce a novel reordering model based on an innovative structure, named, phrasal dependency tree including syntactical and statistical information in context of a log-linear model. The phrasal dependency tree is a new modern syntactic structure b...