Image categorization is the problem of classifying images into one or more of several possible categories or classes, which are defined in advance. Classifiers can be trained using machine learning algorithms, but existing machine learning algorithms cannot work with images directly. We consider a representation based on texture segmentation and a similarity measure which has been used successf...

In this paper we propose a new fusion technique, termed Joint Cohort Normalization Fusion, where the information fusion is done prior to the likelihood ratio test in a speaker verification system. The performance of the technique is compared against two popular types of fusion: feature vector concatenation and expert opinion fusion, for fusion of Mel Frequency Cepstral Coefficients (MFCC), MFCC...

In this study, we propose a new method to predict hairpins in proteins and its evaluation based on the support vector machine. Different from previous methods, new feature representation scheme based on auto covariance is adopted. We also investigate two structure properties of proteins (protein secondary structure and residue conformation propensity), and examine their effects on prediction. M...

We analyze the financial planning problems of young households whose main decisions are how to finance the purchase of a house (liabilities) and how to allocate investments in pension savings schemes (assets). The problems are solved using a multi–stage stochastic programming model where the uncertainty is described by a scenario tree generated from a vector auto-regressive process for equity r...

In this paper, the performance of different predictive vector quantization (PVQ) structures is studied and compared for different degrees of channel noise. Predictive quantization schemes with an auto-regressive (AR) decoder structure are compared with schemes that employ a moving average (MA) decoder. For noisy channels MA prediction performs better than AR. It is shown here that a combination...

In this paper some newly published methods for fault detection and isolation developed for a wind turbine benchmark model are tested, compared and evaluated. These methods have been presented as a part of an international competition. The tested methods cover different types of fault detection and isolation methods, which include support vector machines, observer based methods, and auto generat...

Wind speed forecasting can accurately improve prediction efficiency of wind power in wind farm, decrease failure probability of wind turbine, and extend life cycle. An innovative algorithm is proposed to optimize both the parameters of least squares support vector machine (LSSVM) and the procedure of finding sparse support vector. Firstly, the defects of support vector are analyzed. Then inequa...

In this paper, a method of Synthetic Aperture Radar (SAR) image Automatic Target Recognition (ATR) based on Convolution Auto-encode (CAE) and Support Vector Machine (SVM) is proposed. Using SVM replaces the traditional softmax as classifier CAE model to classify feature vectors extracted by model, which solves problem that less effective in nonlinear case. Since can only solve binary classifica...

rfid technology has great potential to improve the benefits of supply chain management. auto- manufacturing is a competitive industry that every player of it needs great operation efficiency to gain competitive advantage to survive. rfid systems are good enhancements to supply chain processes, therefore it's obvious that every industry has to pay great attention to this it solution, and in this...

I introduce inside money and serially correlated supply shocks to the Uncertain and Sequential Trading (UST) monetary model and test its implications using a vector auto regression impulse response analysis on post-war US data. I find that (a) The importance of money in predicting output is substantially reduced once the stock of inventories is added to the VAR system and (b) Shocks to inventor...