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This paper presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the dataset from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometric vectors, analogous to SVM's support vectors are obtained in order...
An extension of group independent component analysis (GICA) is introduced, where multi-set canonical correlation analysis (MCCA) is combined with principal component analysis (PCA) for three-stage dimension reduction. The method is applied on naturalistic functional MRI (fMRI) images acquired during task-free continuous music listening experiment, and the results are compared with the outcome of the...
This paper explores the theory of learning with similarity functions in the context of common-sense reasoning and natural language processing. Based on this theory, the proposed approach (called Sim-Predictor) is characterized by the process of remapping the input space into a new space which is able to convey the similarity between the input pattern and a number of landmarks, i.e., a subset of patterns...
Understanding the relationship between fMRI BOLD and underlying neuronal activity has been crucial to connect circuit behavior to cognitive functions. In this paper, we modeled fMRI BOLD reconstructions with general linear model and balloon modeling using biophysical models of rat cerebellum granular layer and stimuli spike trains of various response times. Linear convolution of the hemodynamic response...
Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy-inefficient. Here we...
For intelligent vehicle applications, detecting pedestrian technique must be robust and perform in real time. In pedestrian detection, support vector machine (SVM) is one of the popular classifiers because of its robust performance. In this paper, we propose the new method to implement cascade SVM that enables fast rejection of negative samples. The proposed method is tested with INRIA person dataset...
We build a multi-layer architecture using the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn). This model allows great modularity and unique localized learning equations. The multi-layer architecture implements a hierarchical data representation that via belief propagation can be used for learning and inference in pattern completion, correction and classification. We apply the...
This paper presents a new multi-class gene selection and classification method based on multiple support vector machine recursive feature elimination (SVM-RFE). For a multi-class DNA microarray problem, we solve it as multiple binary classification problems. First, the one-versus-all method is used to decompose the multi-class task into multiple binary problems. Second, an SVM-RFE is adopted to select...
In the search space of a complex-valued multilayer perceptron (C-MLP) there exist flat areas called singular regions. Although singular regions cause serious stagnation of learning, there exist descending paths from the regions. Based on this observation, a completely new learning method for C-MLP, called C-SSF1.0, was proposed, making good use of singular regions to stably find excellent solutions...
This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include atmospheric noise (generated by radio emissions due to lightening, for example), radioactive decay, electronic noise and so on, we ‘teach’ a system to approximate the input noise with the...
This paper combines an efficient reinforcement learning algorithm named Multisamples in Each Cell (MEC) with a building thermal comfort control problem. It implements the efficient exploration rule and makes high use of observed samples. A grid is utilized to partition the continuous state into cells that are used to store samples. A near-upper Q function is obtained based on the samples in each cell...
The objective of this investigation is to present an interval-symbolic representation based method for offline signature verification. In the feature extraction stage, Connected Components (CC), Enclosed Regions (ER), Basic Features (BF) and Curvelet Feature (CF)-based approaches are used to characterize signatures. Considering the extracted feature vectors, an interval data value is created for each...
Multi-Column Deep Neural Networks achieve state of the art recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human accuracy. This performance is the result of averaging 11-layers deep networks with hundreds of maps per layer, trained on raw, distorted images to prevent them from overfitting. The entire framework runs on a normal desktop...
Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding...
Object detection is one of the most interesting branches in computer vision. Accurate detection systems can be utilized to various areas. There are two steps in detection, feature extraction and classification. In this paper, new feature extraction method is proposed. Histogram Oriented Gradient (HOG) is famous, fast and accurate feature, but it is not rotation invariant. This paper proposes a new...
As a robust measure of similarity, C-Loss can be successfully used for data fitting such as regression and classification, especially when data contain large outliers. In this paper, we propose a modified C-Loss function, called exponential C-Loss (EC-Loss), which is defined as an exponential function of the C-Loss. The EC-Loss inherits the robustness and smoothness of the C-Loss but may have a better...
Handwritten signature recognition is one important component of biometric authentication. This is a central process in a broad range of areas requiring personal identification, such as security, legal contracts and bank transactions. Extensive efforts have been put into the research towards the verification of handwritten signatures, which contain biometric information. Although many successful methods...
The correct execution of well-defined movements in sport disciplines may increase the body's mechanical efficiency and reduce the risk of injury. While there exists an extensive number of learning-based approaches for the recognition of human actions, the task of computing and providing feedback for correcting inaccurate movements has received significantly less attention in the literature. We present...
In this paper design and implementation of a modular mixed-signal feed-forward neural network is presented. The network is implemented based on the Continuous Valued Number System (CVNS) arithmetic with neurons distributed in the network. Synapse weights are implemented on the chip using capacitive analog memories. Weight values are stored as the CVNS values and are refreshed and updated using the...
Fault detection is a task of major importance in industry nowadays, since that it can considerably reduce the risk of accidents involving human lives, in addition to production and, consequently, financial losses. Therefore, fault detection systems have been largely studied in the past few years, resulting in many different methods and approaches to solve such problem. This paper presents a detailed...
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