Showing papers in "Neurocomputing in 2015"

TL;DR: A supervised method based on feature and ensemble learning is presented to tackle the problem of retinal blood vessel segmentation, which combines two superior classifiers: Convolutional Neural Network and Random Forest.

TL;DR: A novel feature selection algorithm based on Ant Colony Optimization (ACO) called Advanced Binary ACO (ABACO), is presented and simulation results verify that the algorithm provides a suitable feature subset with good classification accuracy using a smaller feature set than competing feature selection methods.

TL;DR: The experimental results show that the Wavelet-SVM approach not only has the best forecasting performance compared with the state-of-the-art techniques but also appears to be the most promising and robust based on the historical passenger flow data in Beijing subway system and several standard evaluation measures.

TL;DR: A novel FOPID controller design method based on an improved multi-objective extremal optimization (MOEO) algorithm for an automatic regulator voltage (AVR) system and the proposed MOEO algorithm is relatively simpler than NSGA-II and single-objectives evolutionary algorithms, such as genetic algorithm, particle swarm optimization (PSO), chaotic anti swarm (CAS) due to its fewer adjustable parameters.

TL;DR: This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes, and shows that the proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods.

TL;DR: This work considers an attacker that aims to maximize the SVM?s classification error by flipping a number of labels in the training data, and formalizes a corresponding optimal attack strategy, and solves it by means of heuristic approaches to keep the computational complexity tractable.

TL;DR: A deep architecture, AU-inspired Deep Networks (AUDN), inspired by the psychological theory that expressions can be decomposed into multiple facial Action Units (AUs), which can achieve state-of-the-art results on all the databases, and validates the effectiveness of AUDN in both lab-controlled and wild environments.

TL;DR: Through extensive empirical studies, it is shown that risk minimization under the 0-1 loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.

TL;DR: A new CAD system that allows the early AD diagnosis using tissue-segmented brain images and is based on several multivariate approaches, such as partial least squares (PLS) and principal component analysis (PCA), which aims to discriminate between AD, mild cognitive impairment (MCI) and elderly normal control (NC) subjects.

TL;DR: A novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification that enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss and is a suitable tool to enhance the performance of the nearest neighbor classifier with big data.

TL;DR: In this algorithm, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm.

TL;DR: The existence and uniqueness of the equilibrium point for fractional-order Hopfield neural networks with time delay are proved and the global asymptotic stability conditions of fractional/time delay neural networks are obtained by using Lyapunov method.

TL;DR: The purpose of this paper is to present specialized measures directed to assess the imbalance level in multilabel datasets (MLDs) and propose several algorithms designed to reduce the imbalance in MLDs in a classifier-independent way, by means of resampling techniques.

TL;DR: The recent progress in visual feature detection is presented and future trends as well as challenges are identified and the relations among different kinds of features are covered.

TL;DR: A hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates is shown.

TL;DR: This work proposes an efficient extension of t-SNE to a parametric framework, kernel t-sNE, which preserves the flexibility of basic t- SNE, but enables explicit out-of-sample extensions and demonstrates that this technique yields satisfactory results also for large data sets.

TL;DR: It is observed that the proposed mammogram classification scheme has a better say with respect to accuracy and area under curve (AUC) of receiver operating characteristic (ROC).

TL;DR: Comparison with typical forecasting methods such as feed forward neural network (FFNN) shows that the proposed method is applicable to the prediction of foreign exchange rate and works better than traditional methods.

TL;DR: The findings reveal that the hybrid optimization strategy proposed here may be used as a promising alternative forecasting tool for higher forecasting accuracy and better generalization ability and to avoid premature convergence.

TL;DR: A supervised machine learning based solution is proposed for an effective spammer detection and shows that the proposed solution is capable to provide excellent performance with true positive rate of spammers and non-spammers reaching 99.1% and 99.9% respectively.

TL;DR: This paper considers the two factors of multi-label feature, feature dependency and feature redundancy, and proposes an evaluation measure that combines mutual information with a max-dependency and min-redundancy algorithm, which allows to select superior feature subset for multi- label learning.

TL;DR: This work proposes an outlier-robust ELM where the l 1 -norm loss function is used to enhance the robustness and the fast and accurate augmented Lagrangian multiplier method is applied to guarantee the effectiveness and efficiency.

TL;DR: This study shows that taking into account such local characteristics of the minority class distribution can be useful both for analyzing performance of ensembles with respect to data difficulty factors and for proposing new generalizations of bagging.

TL;DR: The combination of model-based identification of the robot geometric errors using EKF and a compensation technique using the ANN could be an effective solution for the correction of all robot error sources.

TL;DR: A general learning framework, termed multiple kernel extreme learning machines (MK-ELM), to address the lack of a general framework for ELM to integrate multiple heterogeneous data sources for classification and can achieve comparable or even better classification performance than state-of-the-art MKL algorithms, while incurring much less computational cost.

TL;DR: It is shown empirically that the advantage of using the method proposed in this paper is even clearer when noise features are added, and the proposed method has been compared with other baselines and three state-of-the-art MKL methods showing that the approach is often superior.

TL;DR: This paper investigates the problem of stochastic finite-time state estimation for a class of uncertain discrete-time Markovian jump neural networks with time-varying delays with sufficient conditions for the error dynamics to be stochastically finite- time stable.

TL;DR: A classification approach that hybridizes statistical techniques and SOM for network anomaly detection and Probabilistic Self-Organizing Maps (PSOM) aim to model the feature space and enable distinguishing between normal and anomalous connections.

TL;DR: The results show that the proposed ReliefF extensions improve preceding extensions and overcome some of their drawbacks, and confirm the effectiveness of the proposal for a better multi-label learning.

TL;DR: An ant colony algorithm for synchronous feature selection and parameter optimization for support vector machine in intelligent fault diagnosis of rotating machinery is presented and the advantages of the proposed method are evaluated.