The Very Fast Decision Tree (VFDT) is one of the most important classification algorithms for real-time data stream mining. However, imperfections in data streams, such as noise and imbalanced class distribution, do exist in real world applications and they jeopardize the performance of VFDT. Traditional sampling techniques and post-pruning may be impractical for a non-stopping data stream. To ...

efficiency in agricultural production is indicative of the efficiency level of farm households in their farming activities. farmers in developing countries do not make use of all the potential technological resources, thus making inefficient decisions in their agricultural activities. herein, technical efficiency in relation with the production of three types of rice crop (boro, aus and aman) w...

Pulse Width Modulation (PWM) techniques are commonly used to control the output voltage and current of DC to AC converters. Space Vector Modulation (SVM), of all PWM methods, has attracted attention because of its simplicity and desired properties in digital control of Three-Phase inverters. The main drawback of this PWM technique is &#10its complex and time-consuming computations in real-time ...

This study involves in vitro androgenesis of <sp...

One approach to improving the running time of kernel-based machine learning methods is to build a small sketch of the input and use it in lieu of the full kernel matrix in the machine learning task of interest. Here, we describe a version of this approach that comes with running time guarantees as well as improved guarantees on its statistical performance. By extending the notion of statistical...

Traits related to nitrogen fixation may be used as indirect selection criteria foraflatoxin resistance in peanut. The aim of this study was to investigate therelationship between N2 fixation traits and aflatoxin contamination in peanut underdifferent drought conditions. Eleven peanut genotypes were evaluated under threewater regimes for two seasons in the field. Data were observed on kernel inf...

We propose a novel variant of the conjugate gradient algorithm, Kernel Conjugate Gradient (KCG), designed to speed up learning for kernel machines with differentiable loss functions. This approach leads to a better conditioned optimization problem during learning. We establish an upper bound on the number of iterations for KCG that indicates it should require less than the square root of the nu...

Scientific simulations often produce large volumes of output that are moved to another platform for visualization or storage. This long-distance migration is slow due to the data size and slow network. Compression can improve migration performance by reducing the data size, but compression is computation-intensive and so can raise costs. In this work, we show how to reduce data migration cost b...

At present sequential minimal optimization (SMO) is one of the most popular and efficient training algorithms for support vector machines (SVM), especially for largescale problems. A novel strategy for selecting working sets applied in SMO is presented in the paper. Based on the original feasible direction method, the new strategy also takes the efficiency of kernel cache maintained in SMO into...