Hidden-layer visible deep stacking network optimized by PSO for motor imagery EEG recognition
A novel method called PSO optimized hidden-layer visible deep stacking network (PHVDSN) is proposed for feature extraction and recognition of motor imagery electroencephalogram (EEG) signals. A prior knowledge is introduced into the intermediate layer ...
Soft estimation by hierarchical classification and regression
Classification and numeric estimation are the two most common types of data mining. The goal of classification is to predict the discrete type of output values whereas estimation is aimed at finding the continuous type of output values. Predictive data ...
2,1 norm regularized multi-kernel based joint nonlinear feature selection and over-sampling for imbalanced data classification
High dimensionality and classification of imbalanced data sets are two of the most interesting machine learning challenges. Both issues have been independently studied in the literature. In order to simultaneously explore the both issues of feature ...
A robust regression method based on exponential-type kernel functions
Robust regression methods appear commonly in practical situations due the presence of outliers. In this paper we propose a robust regression method that penalize bad fitted observations (outliers) through the use of exponential-type kernel functions in ...
Multi-objective evolutionary feature selection for online sales forecasting
Sales forecasting uses historical sales figures, in association with products characteristics and peculiarities, to predict short-term or long-term future performance in a business, and it can be used to derive sound financial and business plans. By ...
Unsupervised learning of sensor topologies for improving activity recognition in smart environments
There has been significant recent interest in sensing systems and smart environments, with a number of longitudinal studies in this area. Typically the goal of these studies is to develop methods to predict, at any one moment of time, the activity or ...
Discrete-time optimal adaptive RBFNN control for robot manipulators with uncertain dynamics
In this paper, a novel optimal adaptive radial basis function neural network (RBFNN) control has been investigated for a class of multiple-input-multiple-output (MIMO) nonlinear robot manipulators with uncertain dynamics in discrete time. To facilitate ...
Predictive Nystrm method for kernel methods
Nystrm method is a widely used matrix approximation method for scaling up kernel methods, and existing sampling strategies for Nystrm method are proposed to improve the matrix approximation accuracy, but leaving approximation independent of learning, ...
Local Partial Least Square classifier in high dimensionality classification
A central idea in distance-based machine learning algorithms such k-nearest neighbors and manifold learning is to choose a set of references, or a neighborhood, based on a distance functions to represent the local structure around a query point and use ...
PAC-Bayes bounds for twin support vector machines
Twin support vector machines are regarded as a milestone in the development of support vector machines. Compared to standard support vector machines, they learn two nonparallel hyperplanes rather than one as in standard support vector machines for ...
Detection of pedestrian crossing road
Detection of pedestrian crossing road is the objective of this work. The model incorporates the pedestrian pose recognition and lateral speed, motion direction and spatial layout of the environment. Pedestrian poses are recognized according to the ...
Improved exponential stability criterion for neural networks with time-varying delay
In this paper, the exponential stability for a class of neural networks with time-varying delay is concerned. An improved integral inequality is derived which extends the auxiliary function-based integral inequality. A novel Lyapounov-Krasovskii ...
A Novel multiple kernel-based dictionary learning for distributive and collective sparse representation based classifiers
In recent years, sparse representation theory has attracted the attention of many researchers in the signal processing, pattern recognition and computer vision communities. The choice of dictionary matrix plays a key role in the sparse representation ...
A new image registration algorithm using SDTR
Accurate image registration is a vital step in many computer vision processes. However, traditional SIFT based methods are not able to obtain satisfactory results in some cases. In this paper, we turn the matching problem into a Markov Random Field (MRF)...
SAR target configuration recognition based on the biologically inspired model
How to extract proper features is very important for synthetic aperture radar (SAR) target configuration recognition. However, most of feature extraction methods are hand-designed and usually can not achieve a satisfactory performance. In this paper, we ...
Generalized predictive control of a class of MIMO models via a projection neural network
The system identification and generalized predictive control of a class of multiple input multiple output models are studied. The generalized predictive control problem with unknown parameters is first addressed by finding a control sequence for control ...
Robust stability of hopfield delayed neural networks via an augmented L-K functional
This paper focuses on the issue of robust stability of artificial delayed neural networks. A free-matrix-based inequality strategy is produced by presenting an arrangement of slack variables, which can be optimized by means of existing convex ...
Quasi-uniform synchronization of fractional-order memristor-based neural networks with delay
Quasi-uniform synchronization of delayed fractional-order memristor-based neural networks (FMNNs) is discussed in this paper. On the basis of the theory of fractional differential equations and the theory of differential inclusion, the synchronization ...
Correntropy-based level set method for medical image segmentation and bias correction
This paper presents a novel correntropy-based level set method (CLSM) for medical image segmentation and bias field correction. Firstly, we build a local bias-field-corrected fitting image (LBFI) model in the level set formulation by simultaneously ...
