Figure 3: Image reconstruction results for 2D (upper row) and 3D (lower row) cardiac images comparing L1 minimization in wavelet and finite differences transform domains with the proposed projections in a nonlinear kernel feature space. Root mean squared errors are indicated in the insets. 4497 MR Image Reconstruction Exploiting Nonlinear Transforms Johannes F. M. Schmidt and Sebastian Kozerke ...

This paper proposes a hybrid methodology that exploits the unique strength of the seasonal autoregressive integrated moving average (SARIMA) model and the support vector machines (SVM) model in forecasting seasonal time series. The seasonal time series data of Taiwan’s machinery industry production values were used to examine the forecasting accuracy of the proposed hybrid model. The forecastin...

Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs vari...

Optical remote sensing data have been considered to display signal saturation phenomena in regions of high aboveground biomass (AGB) and multi-storied forest canopies. However, some recent studies using texture indices derived from optical remote sensing data via the Fourier-based textural ordination (FOTO) approach have provided promising results without saturation problems for some tropical f...

In the present work, we propose a scheme for the fusion of different phone duration models, operating in parallel. Specifically, the predictions from a group of dissimilar and independent to each other individual duration models are fed to a machine learning algorithm, which reconciles and fuses the outputs of the individual models, yielding more precise phone duration predictions. The performa...

Support Vector Machines Regression (SVMR) is a learning technique where the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called the -Insensitive Loss Function (ILF), which is similar to loss functions used in the field of robust statistics. The quadratic loss function is well justified under the assumption of Gaus...

In this chapter, we describe various schemes for quantizing speech features to be used in distributed speech recognition (DSR) systems. We have analyzed the statistical properties of MFCCs that are most relevant to quantization, namely the correlation and probability density function shape, in order to determine the type of quantization scheme that would be most suitable for quantizing them eff...

In this paper we present a new approach for modelling scenes with multiple 3D objects from images taken from various viewpoints. Such images are segmented using either supervised or unsupervised algorithms. We consider the mean-shift and support vector machines for image segmentation using the colour and texture as features. Backprojections of segmented contours are used to enforce the consiste...

Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection system for diagnosing heart valve disorders. Phonocardiogram, the digital recording of heart sounds is becoming increasingly popular as it is relatively inexpensive. In this paper, a technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for...

Digital data hiding is a technology being developed for multimedia services, where significant amounts of secure data is invisibly hidden inside a host data source by the owner, for retrieval only by those authorized. The hidden data should be recoverable even after the host has undergone standard transformations, such as compression. In this paper, we present a source and channel coding framew...