In this study, we critically analyse and compare performances of several global optimization (GO) approaches with our hybrid GLPτS method, which uses meta-heuristic rules and a local search in the final stage of find...
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ISBN:
(纸本)9781605580463
In this study, we critically analyse and compare performances of several global optimization (GO) approaches with our hybrid GLPτS method, which uses meta-heuristic rules and a local search in the final stage of finding a global solution. We also critically investigate a Stochastic Genetic Algorithm (StGA) method to demonstrate that there are some loopholes in its algorithm and assumptions. Subsequently, we employ the GLPτS method for neural network (NN) supervised learning, when using our intelligent system for solving real-world patternrecognition and classification problem. In the preprocessing data phase, our system also uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for dimensionality reduction and minimization of the chosen number of features for the classification problem. Finally, the reported results are compared with Backpropagation (BP) to demonstrate the competitive properties and the efficiency of our system. Copyright 2008 ACM.
A novel method is presented to improve the object recognition performance of a biologically inspired model by learning class-specific feature codebook. The feature codebook is multi-class shared in the original model,...
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ISBN:
(纸本)9781424429271
A novel method is presented to improve the object recognition performance of a biologically inspired model by learning class-specific feature codebook. The feature codebook is multi-class shared in the original model, and the content proportion for different codeword type is set in uniform distribution. According to corresponding discriminability, the codebook content proportion is adjusted upon different codeword types (feature vector sizes and filter scales). The test results demonstrate that the codebooks built with proposed modification achieve higher total-length efficiency.
In this paper, we introduce the application of transformation patternrecognition based on a complex artificial immune system. The key feature of the complex artificial immune system is the introduction of complex dat...
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In this paper, we introduce the application of transformation patternrecognition based on a complex artificial immune system. The key feature of the complex artificial immune system is the introduction of complex data representation. We use complex numbers as the data representation instead of binary numbers used before, besides the weight between different layers. The complex partial autocorrelation coefficients of input antigen which are considered as the antigen presentation are calculated in major histocompatibility complex (MHC) layer of the complex artificial immune system. In the simulations, the transformation of patterns, such as translation, scale or rotation, are recognized in much higher accuracy, and it has obviously higher noise tolerance ability than traditional real artificial immune system and even the complex PARCOR model.
In this paper, Multi-class classification using an Improved Multiobjective Simultaneous learning framework (MCIMSDC) is proposed. This learning algorithm is used to solve any multiclass classification problem. It is b...
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ISBN:
(纸本)9788132204909
In this paper, Multi-class classification using an Improved Multiobjective Simultaneous learning framework (MCIMSDC) is proposed. This learning algorithm is used to solve any multiclass classification problem. It is based on the framework proposed by Cai, Chen and Zhang [1] in 2010. In [1], the multiple objective functions are utilized to simultaneously optimize the clustering and classification learning by employing Bayesian theory. In [1], the selection of learning parameter i.e., clusters membership degree u(j)(x(i)) is initially chosen at random due to which the number of iteration and training time achieve while obtaining the stable cluster center is comparatively higher, but here in the proposed methodology, the value of clusters membership degree u(j)(x(i)) is calculated on the basis of randomly initialized cluster centers. Thus, these cluster centers are updated and corresponding u(j)(x(i)) is calculated iteratively. Experimental results show that, this method improve the performance by significantly reducing the number of iterations and training time required to obtain the cluster center. The same is being verified with six benchmark datasets.
The Internet-based security soft-i-Robot is modeled using softcomputing paradigms for problem solving and decision-making in complex and ill-structured situations. soft-i-Robot monitors the workspace with multimedia ...
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ISBN:
(纸本)9781479925834
The Internet-based security soft-i-Robot is modeled using softcomputing paradigms for problem solving and decision-making in complex and ill-structured situations. soft-i-Robot monitors the workspace with multimedia devices and sensor using an Internet application program. The model has sensory subsystems such as Intruder detection which, detects intruder, captures image and sends to server, and an Obstacle Avoidance Unit to detect the objects in the path of the mobile robot. These multiple features with hybrid softcomputing techniques depart the developed soft-i-Robot from the existing developments, proving that the streaming technology-based approach greatly improves the sensibility of robot tele-operation. The relatively powerful online robots available today provoke the simple question, in terms of two competing goals: recognition accuracy and computing time. Improved recognition accuracy and reduced computing time for face recognition of the intruder is obtained using Morphological Shared Weight Neural Network. To obtain a collision-free optimized path, soft-i-Robot uses derivative free Genetic Algorithm. With rapid expansion of Robotics and softcomputing paradigms, robotic technology touches upon self-understanding of humans, socio-economic, legal and ethical issues leading to improved performance rate and information processing capabilities.
Interpolation Search is an algorithm that performs the task of searching for a data through a sorted array by opting a divide and conquer approach, meaning filtering out data to reduce the sample size through unique d...
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ISBN:
(纸本)9781665421973
Interpolation Search is an algorithm that performs the task of searching for a data through a sorted array by opting a divide and conquer approach, meaning filtering out data to reduce the sample size through unique deductions. In the case of interpolation search specifically, the procedure is to assume the array to be in the form of a perfect arithmetic progression series and then predict the ideal position of the key element. This paper proposes a new searching technique which is an advancement of interpolation search, in the name of 'attern recognition Search'. We look to highlight some of the key drawbacks of interpolation search which are accounted for in the patternrecognition Search.
The proceedings contain 137 papers. The topics discussed include: a new clustering method based on weighted kernel K-means for non-linear data;a review of recent alignment-free clustering algorithms in expressed seque...
ISBN:
(纸本)9780769538792
The proceedings contain 137 papers. The topics discussed include: a new clustering method based on weighted kernel K-means for non-linear data;a review of recent alignment-free clustering algorithms in expressed sequence tag;league championship algorithm: a new algorithm for numerical function optimization;an improved discrete particle swarm optimization in evacuation planning;PSO-based optimization of state feedback tracking controller for a flexible link manipulator;a novel fuzzy histogram based estimation of distribution algorithm for global numerical optimization;rule modeling engine for optimizing complex event processing patterns;correlation research of association rules and application in the data about coronary heart disease;linear antenna array synthesis with invasive weed optimization algorithm;robust type-2 fuzzy control of an automatic guided vehicle for wall-following;and a modified differential evolution algorithm and its application to engineering problems.
In this paper, we present a novel and original framework for computing Local Binary pattern (LBP)-like patterns on a triangular mesh manifold. This framework, that we called mesh-LBP, can be adapted to all the LBP var...
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ISBN:
(纸本)9781479952083
In this paper, we present a novel and original framework for computing Local Binary pattern (LBP)-like patterns on a triangular mesh manifold. This framework, that we called mesh-LBP, can be adapted to all the LBP variants employed in 2D image analysis. As such, it allows extending the related techniques to mesh surfaces. First, we describe the foundations, the construction and the features of the mesh-LBP. In the experiments, we first show evidence of the presence of the "uniformity" aspect in the mesh-LBP patterns. Then, we report about the application of mesh-LBP to the problem of 3D texture-classification in comparison to standard 3D surface descriptors and show the mesh-LBP robustness to mesh irregularities.
This paper deals with the comparison of two different approaches for multi-task patternrecognition problem-multi-label and multi-perspective. The experiment performed measured the hamming loss and mean accuracy of bo...
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ISBN:
(纸本)9783319262277;9783319262253
This paper deals with the comparison of two different approaches for multi-task patternrecognition problem-multi-label and multi-perspective. The experiment performed measured the hamming loss and mean accuracy of both classifiers, to judge which of these two better fit to this kind of problem.
GDPLL(k) grammars have been introduced as a tool for the construction of syntactic patternrecognition-based systems. The grammars have been successfully used in several different applications. The practical experienc...
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ISBN:
(纸本)9783319591629
GDPLL(k) grammars have been introduced as a tool for the construction of syntactic patternrecognition-based systems. The grammars have been successfully used in several different applications. The practical experience with the implementation of a syntactic patternrecognition system based on GDPLL(k) grammars has served to define methodological guidelines for constructing such systems. In the paper key methodological issues are presented.
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