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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...
Considering the simplicity and fast training speed of Haar-like features, the high detecting precision of HOG features, a combined method is proposed on the basis of the two features. Several rectangular features which can describe local human characteristics based on original features are added. The combined method can retain the precision of HOG features and increase the speed of detection at the...
The multicollinearity exists in the interpretive variable of regression model , it often brings inconvenience to social post-evaluation. The ridge regression has advantages than LS method. The support vector machines (SVM) is a novel machine learning tool in data mining. It is based on the structural risk minimization (SRM) principle, which has been shown to be more superior than the traditional empirical...
SVM is a novel type of statistical learning method that has been successfully used in speaker recognition. However, training SVM consumes long computing time and large storage space with all training examples. This paper proposes an improved sparse least-squares support vector machine (LS-SVM) for speaker identification. Firstly KPCA is exploited to reduce the dimension of input vectors and to denoise...
RFID localization is a promising new field of work that is eagerly awaited for many different types of applications. For use in a medical context, special requirements and limitations must be taken into account, especially regarding accuracy, reliability and operating range. In this paper we present an experimental setup for a medical navigation system based on RFID. For this we applied a machine...
In this article, a new Multi Agent System based on Online Sequential Extreme Learning Machine (OSELM) neural network and Bayesian Formalism (MAS-OSELM-BF) is introduced. It is an improvement of a single OSELM (single agent) by combined multiple OSELMs (multi agents) with a final decision making module (parent agent). Here, the development of the parent agent is motivated by the Bayesian Formalism...
Web automation programs offer a means for users to enhance the usability of the web. These programs can be published on a wiki or other repository, thereby making them available for use by other users. However, in addition to programs of broad usefulness to the community at large, these repositories also contain many programs that are unreliable or highly specialized to the needs of very small sub-...
In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the...
High performance biometrics helps in reliably identifying persons for access authorization and other purposes. Iris recognition is very effective in identifying persons due to the iris' unique features and the protection of the iris from the environment and aging. We focus on the design and training of a feed-forward artificial neural network for high-performance iris recognition and investigate the...
In real world classification tasks, the original instances are represented by raw features. Usually domain related algorithms are needed to extract discriminative features. But the algorithms selection and additional parameters tuning are difficult for people with little domain knowledge and experience. In this paper, a new machine learning framework called "decompose learning" is proposed...
The generalization ability of neural network is an important aspect affecting its application. Meanwhile, the selection of training samples has a great impact on this ability. In order to improve the completeness of training samples, a method of samples self-learning of BP neural network based on clustering is put forward in this paper. By using the method of clustering, new samples can be collected...
In recent years, the use of machine learning algorithms (classifiers) has proven to be of great value in solving a variety of problems in software engineering including software faults prediction. This paper extends the idea of predicting software faults by using an ensemble of classifiers which has been shown to improve classification performance in other research fields. Benchmarking results on...
This paper describes an analysis of body radiation frequency for the purpose of gender classification. The human radiation frequency is experimentally studied from 33 healthy human subjects of 17 males and 16 females. KNN classifier is employed for gender classification. The number of training to testing ratio was evaluated at 50 to 50, 60 to 40 and 70 to 30, to determine best classification accuracy...
Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost,...
Machine learning techniques have recently been applied to the problem of lithographic hotspot detection. It is widely believed that they are capable of identifying hotspot patterns unknown to the trained model. The quality of a machine learning method is conventionally measured by the accuracy rates determined from experiments employing random partitioning of benchmark samples into training and testing...
In addressing side information based face recognition scenario, a new Margin Emphasized Metric Learning (MEML) method is proposed. As an improvement of previous metric learning, MEML defines a new objective function for optimization, which adds more weights to sample pairs on the boundary thus hard to classify. To further improve face verification performance, MEML is applied to Gabor feature in a...
Decoding perceptual or cognitive states based on brain activity measured using functional Magnetic Resonance Imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli. However, the high dimensionality and intrinsically low Signal-to-Noise Ratio (SNR) of fMRI data poses great challenges to such techniques. The problem is aggravated in the case of multiple...
In recent years, machine learning technology often used as a recognition method of anomaly in anomaly detection. In this paper we have proposed a One-class small hypersphere support vector machine classifier (OCSHSVM) algorithm, which builds a learning classifier model via both normal and abnormal network traffic. This combination of normal and abnormal traffic for training model gives the better...
Smart Grid with Time-of-Use (TOU) pricing brings new ways of cutting costs for energy consumers and conserving energy. It is done by utilities suggesting the user ways to use devices to lower their energy bills keeping in mind its own benefits in smoothening the peak demand curve. However, as suggested in previous related research, user's comfort need must be addressed in order to make the system...
Detecting anomalous traffic on the Internet has remained an issue of concern for the community of security researchers over the years. Advances in computing performance, in terms of processing power and storage, have allowed the use of resource-intensive intelligent algorithms, to detect intrusive activities, in a timely manner. Naïve Bayes is a statistical inference learning algorithm with promise...
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