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This paper describes an algorithm that parallelizes support vector machines. The data is split into subsets and optimized separately with multiple SVMs, instead of analyzing the whole training set in one optimization step. The partial results are combined and filtered in a cascade of SVMs. The process terminates when the global optimum is reached. The Cascade SVM is spread over multiple processors...
Feature selection algorithm has a great influence on the accuracy of text categorization. The traditional information gain (IG) feature selection algorithm usually selects the features that rarely appear in the specified categories, but frequently appear in other categories. To overcome this drawback, on the basis of in-depth analysis of the related algorithms, an improved IG feature selection method...
Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary -- also known as hyperplane -- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
Identification of minimum number of local regions of a handwritten character image, containing well-defined discriminating features which are sufficient for a minimal but complete description of the character is a challenging task. A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here. The powerful framework of Harmony Search has been utilized...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
The popularity of social computing and sentiment analysis has attracted an increasing attention of tourism industry and academia. The sentiment analysis of residents' and tourists' plays an important role to the development of tourism. It aims to identify and analyze opinions and emotions contained in reviews which are expressed by residents or tourists. Although it's a challenging task, many companies...
With the emerging increase of diabetes, that recently affects around 346 million people, of which more than one-third go undetected in early stage, a strong need for supporting the medical decision-making process is generated. A number of researches have focused either in using one of the algorithms or in the comparisons of the performances of algorithms on a given, usually predefined and static datasets...
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted...
In this paper, an automated model selection approach guided by Cuckoo search is proposed for k-nearest neighbor (KNN) learning algorithm. The performance of KNN mostly depends on the value of k and the distance metric used. The values of these parameters are computed by optimizing an objective function designed for measuring the classification accuracy of KNN. Cuckoo search being an efficient optimization...
In literature, there are many supervised learning algorithms presented and applied in various problem domains. However, none of them could consistently perform well over all the datasets. This paper presents a novel approach for simultaneous selection of optimal feature subset and classifier for a given dataset. For large scale problems, this would require to search a huge solution space. Therefore,...
A masquerade attacker impersonates a legal user to utilize the user services and privileges. The semi-global alignment algorithm (SGA) is one of the most effective and efficient techniques to detect these attacks but it has not reached yet the accuracy and performance required by large scale, multiuser systems. To improve both the effectiveness and the performances of this algorithm, we propose the...
Recent developments in radio technology and processing systems, Wireless Sensor Networks (WSNs) are tremendously being used to perform an assortment of tasks from their atmosphere. Localization plays the most important task in WSNs. Accuracy is the one of the major problems facing localization. In this paper, we propose an improved localization algorithm based on the learning concept of support vector...
Class imbalance presents a problem when traditional Classification algorithms are applied .In the previous years there are most important substitution and change has been carried out on data classification. Classification of data becomes difficult because of its unbalanced nature. The problem of imbalance class has developed into significant data mining issue. The class imbalance situation arises...
The major branch of data mining used to assign raw data to a particular group is classification. It is a method used to forecast the group association for data objects. Medical Imaging is dealing with the designing of automated systems to help physician diagnosis. In this paper, we present the comprehensive study of some classification techniques used in Medical Imaging. Several types of classification...
In this paper, a single hidden-layer feedforward fusion network is proposed for face identity verification. Essentially, the feature extraction, matching score calculation and fusion algorithm design steps are integrated and absorbed into a hidden layer of the model. Each hidden node works on the raw face image directly and produces an Euclidean distance based match score within the network. These...
In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. These deep learning approaches have been applied to image recognition, voice recognition and text processing. However, to our knowledge, the deep learning approaches have not been extensively studied for web data. In this paper, we apply deep belief networks...
Predictability of home energy usage forms the basis of many home energy management and demand-response systems. While existing studies focus on designing more accurate prediction algorithms, a comprehensive energy management solution requires a broad understanding of prediction accuracy at different granularities, for example appliance and home, as well as different time horizons, for example an hour,...
Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support...
Until now, designing a reliable image segmentation algorithm is still an open problem. Research related to this matter is still underway, but in one occasion we may be faced with the problem for selection image segmentation algorithms that will we use? To get the solution of this problem we need a good technical evaluation of image segmentation algorithms. With the technique, it is expected we can...
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