Support vector machine (SVM) classifiers represent one of the most powerful and promising tools for solving classification problems. In the past decade SVMs have been shown to have excellent performance in the field of data mining. The standard SVM classifier treats all instances equally. However, in many applications we have different levels of confidence in different instances that belong to ...

In this paper a hierarchical classification framework using the feature-weighting tree for the objective of applying diverse weighting to acoustic features is proposed for speech recognition. The hierarchical feature-weighting tree with a flexible structure complexity can be constructed optimally with the optimal splitting for the recognition confusion graph. Based on the minimum classification...

Optimizing weighting factors for a linear combination of terms in a scoring function is a crucial step for success in developing a threading algorithm. Usually weighting factors are optimized to yield the highest success rate on a training dataset, and the determined constant values for the weighting factors are used for any target sequence. Here we explore completely different approaches to ha...

Partial Transmit Sequence (PTS) is a promising technique to reduce Peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. However optimum PTS (OPTS) needs exhaustive search over all combinations of allowed phase weighting factors. It results in high computational complexity. Grouping Phase Weighting (GPW) technique is a method which has reduced computat...

In this paper the novel method of “weighted OFDM” is addressed. Different types of weighting factors (including Rectangular, Bartlett, Gaussian, Raised cosine, Half-sin and Shanon) are considered. The impact of weighting of OFDM on the peak-to-average power ratio (PAPR) is investigated by means of simulation and is compared for the above mentioned weighting factors. Results show that by weighti...