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Sparse coding has proved its efficiency in the image classification task. However, its major drawback is the discarding of the spatial context information that can be extracted from the image. Therefore, we propose in this work a novel sparse coding method called Laplacian sparse coding based on the integration of topological information in the encoding process. This is achieved by embedding the similarities...
The paper proposes an architecture for a multiplier-adder network that can be used for the design of a digital neuron cell. The core of the multiplier is based on a hybrid memristor network, in which digital CMOS logic is combined with multi-stable storing memristor devices. The multi-bit storing feature of memristors is favoured since it simplifies the realisation of ternary data. Using such a ternary...
The similarity comparisons between single-task walking and dual-task walking on Alzheimer's disease (AD) patients has been commonly performed for cognitive declination measurement. This paper presents a personalized gait similarity measurement approach based on Hidden Markov model for the self-comparison between the single-task walking and dual-task walking. Compared with traditional approaches which...
A pseudo-inverse linear discriminants has nothing in common with a Fisher linear discriminant (FLD) if the desired outputs of each sample are changeable. With the customarily desired outputs {1, −1}, a simple and size-related threshold is acquired, which. Multiple thresholds related to sample sizes and distribution regions are thus developed, and the optimal ones may be singled out from among by means...
Reasoning is a high-level cognitive function that is gaining attention in the artificial neural network community. While there are many types of reasoning, this paper is specifically looking at valid categorical syllogisms. First we show that a standard bi-directional associative memory cannot learn all valid categorical syllogisms because these syllogisms are not linearly separable. Therefore a more...
Proposing efficient methods for fire protection is becoming more and more important, because a small flame of fire may cause huge problems in social safety. In this paper, an effective fire flame detection method is investigated. This fire detection method includes four main stages: in the first step, a linear transformation is applied to convert red, green, and blue (RGB) color space through a 3*3...
Deep learning methods allow a classifier to learn features automatically through multiple layers of training. In a deep learning process, low-level features are abstracted into high-level features. In this paper, we propose a new probabilistic deep learning method that combines a discriminative model, namely, Support Vector Machine (SVM), with a generative model, namely, Gaussian Mixture Model (GMM)...
Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper,...
This paper provides a new approach to reconstruct a fluid field from sparse sensor observations. Using the extreme learning machine (ELM) autoencoder, we can extract a dominant basis of the fluid field of interest from a database consisting of a series of fluid field snapshots obtained from offline computational fluid dynamics (CFD) simulations. The output weights of ELM autoencoder can be viewed...
Policy evaluation has long been one of the core issues of the online reinforcement learning, especially in the continuous state domain. In this paper, the issue is addressed by employing Gaussian processes to represent the action value function from the probability perspective. By modeling the return as a stochastic variable, the action value function can sequentially update according to observed...
This paper presents a neurodynamic optimization approach with two coupled recurrent neural networks for the synthesis of linear systems with fault detection via robust pole assignment. The proposed approach is shown to be capable of synthesizing control systems with robust state estimators and fault detection with parameter perturbation. The operating characteristics of the recurrent neural networks...
Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control...
Brain waves are classified as gamma, beta, alpha, theta, and delta waves to quantify brain activity and can be approximated as sinusoidal waves of different frequencies. In this work, we use sinusoidal waves at two different frequencies to control chaos in a chaotic neural network (CNN) to explore the effect of multi-frequency sinusoidal waves in chaos control. We propose two methods to control chaos...
Although deep neural networks (DNNs) have achieved great performance gain, the immense computational cost of DNN model training has become a major block to utilize massive speech data for DNN training. Previous research on DNN training acceleration mostly focussed on hardware-based parallelization. In this paper, node pruning and arc restructuring are proposed to explore model redundancy after a novel...
We propose a parallel training framework of convolutional neural networks (CNNs) for small sample learning. In the framework we model the feature filter process and show Sadowsky energy distribution exists in the model. Using Sadowsky energy distribution, the weights in convolutional kernels can be rearranged after each update according to special cases. With this rearrangement, each CNNs in the framework...
Both the type and location of synaptic contacts can have profound functional consequences for cortical pyramidal neurons. Axo-spinous synapses integrate evoked potentials in a linear fashion and axo-shaft ones summate responses sublinearly. Using modeling and simulation, we accurately replicate such type-dependent synaptic integration and examine one of the possible underlying mechanisms. Moreover,...
We proposed a Deep Self-Organizing Map (DSOM) algorithm which is completely different from the existing multi-layers SOM algorithms, such as SOINN. It consists of layers of alternating self-organizing map and sampling operator. The self-organizing layer is made up of certain numbers of SOMs, with each map only looking at a local region block on its input. The winning neuron's index value from every...
Twin support vector machines are a powerful learning method for binary classification. Compared to standard support vector machines, they learn two hyperplanes rather than one as in standard support vector machines, and work faster and sometimes perform better than support vector machines. However, relatively little is known about their theoretical performance. As recent tightest bounds for practical...
The detection of brain responses corresponding to the presentation of a particular class of images is a challenge in Brain-Machine Interface (BMI). Brain decoding is nowadays possible thanks to advanced brain recording devices (fMRI, EEG, MEG), and the use of appropriate signal processing and machine learning techniques. Current systems based on the detection of brain responses during rapid serial...
Error bounds based on worst likely assignments use permutation tests to validate classifiers. Worst likely assignments can produce effective bounds even for data sets with 100 or fewer training examples. This paper introduces a statistic for use in the permutation tests of worst likely assignments that improves error bounds, especially for accurate classifiers, which are typically the classifiers...
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