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Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research....
Apache Spark is an open source distributed data processing platform that uses distributed memory abstraction to process large volume of data efficiently. However, performance of a particular job on Apache Spark platform can vary significantly depending on the input data type and size, design and implementation of the algorithm, and computing capability, making it extremely difficult to predict the...
Two advanced modelling approaches, Multi-Level Models and Artificial Neural Networks are employed to model house prices. These approaches and the standard Hedonic Price Model are compared in terms of predictive accuracy, capability to capture location information, and their explanatory power. These models are applied to 2001–2013 house prices in the Greater Bristol area, using secondary data from...
With an ever-increasing amount of information made available via the Internet, it is getting more and more difficult to find the relevant pieces of information. Recommender systems have thus become an essential part of information technology. Although a lot of research has been devoted to this area, the factors influencing the quality of recommendations are not completely understood. This paper examines...
Aim at the ill-posedness of vegetation biophysical variables inversion problems, the paper presents a multi-scale, multistage (MSMS) inversion approach based on field data, multi-resolution remotely sensed observations and spatial knowledge for estimating crop leaf area index (LAI). The proposed MSMS inversion method takes advantage of multiple stages inversion strategy and prior information. Firstly,...
An Artificial Neural Network (ANN) algorithm for the Soil Moisture Content (SMC) retrieval from the C-band EPS-SG SCA scatterometer, which will replace the Metop ASCAT, was implemented and tested with real data and model simulations. The main aim of this activity was in understanding the potential of VH channel, which inclusion on the mid-beam antenna of EPS-SG SCA is currently being considered, for...
Deep Belief Network (DBN) is a classic deep learning model, and it can learn higher feature and do better classification job. We combine DBN's basic component Restricted Boltzmann Machines (RBM) with the statistic distribution of Polarimetric SAR (PolSAR) data. Based on it, we develop a deep learning classification method that is suitable for PolSAR data. To verify the effectiveness of the method,...
Crop growth stages are important factors for segmenting the crop growing seasons and analyzing their growth conditions against normal conditions by periods. Time series of high temporal resolution, up to daily, satellite remotely sensed data are used in establishing crop growth estimation model and estimate the growth stages. The daily surface reflectance data from Moderate Resolution Imaging Spectroradiometer...
Latent variable (LV) models such as partial least squares (PLS) have been widely used to derive low-dimensional subspaces and build regression models in process control problems, especially in quality prediction tasks. However, they are based on the assumption that industrial processes operate at steady states, thereby ignoring process dynamics. In this article, slow feature regression (SFR), a novel...
The development of a model classification intrusion detection using Weighted Extreme Learning Machine was examined with KDD'99 data set ad 4 types of main attack : Denial of Service Attack (DoS), User to Root Attack (U2R), Remote to Local Attack (R2L), and Probing Attack, when comparing the effectiveness of working process of the method presented to SVM+GA[6] and ELM, found that weighted technique...
In low resource Automatic Speech Recognition (ASR), one usually resorts to the Statistical Machine Translation (SMT) technique to learn transform rules to refine grapheme lexicon. To do this, we face two challenges. One is to generate grapheme sequences from the training data as the targets, which is paired with the original transcripts to train SMT models; the other is to effectively prune the learned...
Currently, various perspectives of neural networks are proposed for solving classification problems. Some of them are based on two types of mapping functions, namely, linear and nonlinear, for mapping an input space into a feature space. In addition, some neural networks are proposed based on probability theory. Since some models are appropriated for some kinds of data, depending on a distribution...
Chronic Respiratory Disease (CRD) is a serious problem in broiler farms and food production industry. This disease cannot be observed easily during the broiler raised process. The model for predicting CRD rate is not exactly identified, because of the variation in farm environment and the development of breeding. Therefore the embedded of concept drift to the normal predictor is the possible way to...
In recent years, the damage caused by botnets has increased and become a big problem. To solve this problem, we proposed a method to detect unjust C&C servers by using Hayashi's quantification theory class II. This method is able to detect unjust C&C servers, even if they are not included in a blacklist. However, it was predicted that the detection rate for this method decreases with...
Nonlinearity of power flow equations is one of the major underlying factors in a power systems operation complexity. The need for a robust and less complex models rises in a volatile, dynamic and real time scenario. This paper introduces new empirical models using multivariate linear regression (MLR) methods with least squares for both real and reactive branch flows. The models do not make prior assumptions...
With the rapid development in science and technology, data acquisition, storage and mining technology are widely applied to various fields. All aspects of people's lives are recorded as data. Through the analyzing and arranging of data, people can get a lot of valuable information. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM) and partial least squares (PLS)...
The distribution grid is changing to become an active resource with complex modeling needs. The new active distribution grid will, within the next ten years, contain a complex mix of load, generation, storage and automated resources all operating with different objectives on different time scales from each other and requiring detailed analysis. Electrical analysis tools that are used to perform capacity...
In many small dataset learning tasks, however, owing to the incomplete data structure, the explicit information for decision making is limited. This research aims to learn more information hidden inside the incomplete data by adding more samples to strengthen data structures. Based on the prior knowledge provided by the M5' model tree, the proposed research mechanism generates artificial samples to...
Most of the empirical topographic correction methods are based on the universal assumptions of the relationship between radiations and solar incident angles. The correction accuracy is hardly to be accessed quantitatively. This paper introduces a land cover adaptive C (LCAC) method for topographic correction, and verifies its advantage quantitatively. Experiments on synthetic and real remote sensing...
An restricted Boltzmann machine learning algorithm were proposed in the two-lead heart beat classification problem. ECG classification is a complex pattern recognition problem. The unsupervised learning algorithm of restricted Boltzmann machine is ideal in mining the massive unlabelled ECG wave beats collected in the heart healthcare monitoring applications. A restricted Boltzmann machine (RBM) is...
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