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Microarray data classification based on principal curves

Microarray data classification based on principal curves

引用

international conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yunsong Qi Lei Pan Huaijiang Sun School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology of PLA Nanjing China

In this paper, a novel classifier is proposed to classify microarray data using principal curves. Principal curves are the non-linear generalization of principal components. Intuitively, a principal curve `passes through the middle of the data cloud'. As a kind of new classification technique, Principal Curve-based classifier (PC) involves a novel way of computing a principal curve for each class using the training data. A test sample is given the class-label of the principal curve that is closest to it according to Expected Squared Error. Experimental results illustrate the performance of the PC is better than other existing approaches when a very small sample size is concerned.

关键词： Support vector machines Bayesian methods Training data Cancer Niobium pattern recognition Gene expression

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Local Directional pattern (LDP) – A Robust Image Descriptor for Object recognition

Local Directional Pattern (LDP) – A Robust Image Descriptor...

引用

IEEE conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Taskeed Jabid Md. Hasanul Kabir Oksam Chae Department of Computer Engineering Kyung Hee University South Korea

This paper presents a novel local feature descriptor, the Local Directional pattern (LDP), for describing local image feature. A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy situation. A rotation invariant LDP code is also introduced which uses the direction of the most prominent edge response. Finally an image descriptor is formed to describe the image (or image region) by accumulating the occurrence of LDP feature over the whole input image (or image region). Experimental results on the Brodatz texture database show that LDP impressively outperforms the other commonly used dense descriptors (e.g.,Gabor-wavelet and LBP).

关键词： Image edge detection Histograms Pixel Robustness Lighting Databases Noise

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Smart Swap for more efficient clustering

Smart Swap for more efficient clustering

引用

international conference on Green Circuits and Systems (ICGCS)

作者： Jinhua Chen Qinpei Zhao Pasi Fränti School of Computing University of Eastern Finland Joensuu Finland

Local search algorithms, such as randomized and deterministic swap-based clustering, are often used for solving clustering problem. In this paper, we propose a new swap-based local search algorithm, Smart Swap, which preserves the stability of the previous solutions but is more efficient. It performs the swap by finding the nearest pair among the centroids and sorting the clusters by their distortion values. Then it swaps one of the nearest pair centroids to any position in that cluster. K-means iteration is employed to repartition the dataset and to fine-tune the swapped solution. The algorithm is easy to implement and iterates less than the previous swap-based local search algorithms. Experiments show that the proposed algorithm keeps at least 97% stability for the synthetic datasets and 0.577 of standard deviation for the real data. It is also much faster than the other swap-based algorithms.

关键词： Clustering algorithms Partitioning algorithms Machine learning algorithms Stability Cost function Sorting Machine learning Data mining pattern recognition Computational efficiency

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Power quality disturbances classification using specialized neural networks

Power quality disturbances classification using specialized ...

引用

international conference on Harmonics and Quality of Power

作者： Augusto S. Cerqueira Danton D. Ferreira Marcia B. S. Pinto Marlon L. G. Salmento José M. de Seixas Department of Electrical Circuits Federal University of Juiz de Fora Juiz de Fora Minas Gerais Brazil Automation and Control Engineering Federal University of Lavras Lavras Minas Gerais Brazil Department of Electrical and Computing Engineering Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781424472444

This work presents an automatic classification system for power quality disturbances based on specialized neural networks. The proposed system is capable to classify multiple disturbances, which is very important in some power systems scenarios. A system was designed and tested with synthetic signals generated by software and the global efficiency achieved was 98.9%. The system was also evaluated with real data achieving good results.

关键词： Artificial neural networks Power quality Equations pattern recognition Transient analysis Harmonic analysis

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Classification of Audio Data Using a Centroid Neural Network

Classification of Audio Data Using a Centroid Neural Network

引用

international conference on Information Science and Applications (ICISA)

作者： Dong-Chul Park Intelligent Computing Research Laboratory Department of Electronics Engineering Myongji University Yongin si South Korea

ISBN: (纸本)9781424459421;9781424459438

The automatic classification of audio data is an effective way to organize a large-scale audio data files. In this paper, an automatic content-based audio classification model using Centroid Neural Networks (CNN) with a Divergence Measure is proposed. The Divergence-based Centroid Neural Network (DCNN) algorithm, which employs the divergence measure as its distance measure, is used for clustering of Gaussian Probability Distribution Function (GPDF) data. In comparison with other conventional algorithms, the D-CNN designed for probability data has the robustness advantages of utilizing a audio data representation method in which each audio data is represented by a Gaussian distribution feature vector. Experiments and results show that the proposed classification model very compatible classification accuracy with classical models employing the conventional k-means and CNN algorithms.

关键词： Neural networks Multiple signal classification Cellular neural networks Feature extraction Music information retrieval Discrete wavelet transforms Clustering algorithms Robustness pattern recognition Brightness

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Faster Convergence of BP Network with Hybridization of Improved Cost Function and Control Memory Adaptation

Faster Convergence of BP Network with Hybridization of Impro...

引用

Asia international conference on Modeling & Simulation

作者： Shafaatunnur Hasan Siti Mariyam Hj. Shamsuddin Soft Computing Research Group K-Economy Research Alliance Universiti Teknologi Malaysia Skudai Malaysia

ISBN: (纸本)9781424471966;9780769540627

Due to the weaknesses of Neural Network (NN) learning, this paper proposes an alternative approach in enhancing NN learning by integrating improved cost function with control adaptation of the nodes and address memory. As commonly known, weight adjustments of NN particularly in Back propagation (BP) algorithm, involve the connections between neurons, the activation function used by the neurons, the learning algorithm that specifies the procedure for adjusting the weights and the cost functions. The cost functions of BP are calculated based on the derivatives. These derivatives will determine the success rate of the application to train the network with an error function that resembles the objective of the problem on hand. Due to that, the concept of weights governance with control part mechanism between the input and hidden layer, and unit offsets of the hidden layer are implemented. to alleviate the problems of BP learning. The address memory part of the network will detain the output pattern of the hidden layer. Subsequently, the output patterns are compared with the input pattern, and propels back to the output layer after learning. From the experiments, we found that the results are promising with these mechanisms and improved cost function which yields faster convergence rates.

关键词： Convergence Cost function Neural networks Backpropagation algorithms Neurons Computer networks Weight control Optimization methods Asia Analytical models

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A model for international relationships based on Intelligence Ball theory

A model for international relationships based on Intelligenc...

引用

international conference on Industrial Mechatronics and Automation, ICIMA

作者： Li Dehua Huang Gang Wu Shihong Ruan Jun Institute of Pattern Recognition and Artificial Intelligence HuaZhong University of Science and Technology Wuhan China Information Engineering School Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781424476534

In order to service in the analysis and simulation of modern international relationship by using high-tech cluster of information technology, A Intelligence Ball Computation Model (IBCM) is used to describe the international relationship, this approach leads to the usage of numerical methods in this paper. The idea of the design is to combine Complex Adaptive Systems theory at the microcosmic level with qualitative System Dynamics at the macrocosmic level. international-field Agent (it corresponds to the international community in the real world.) and Ball Agents encapsulate the microcosmic model of activities at in Politics of Military Confrontation. Force Agents (countries or regions) and Environment Agent (strategic environment) encapsulate the microcosmic world. The distinct Force Agents carry on the interactions of Five Elements through the medium -- the international-field Agent, which reflect the confrontation in Ball Agents on the international-field, hereby this implement the link between the macrocosmic level and the microcosmic level. We used a SA-PSO Algorithm to optimize strategies for Multi-Agents.

关键词： Artificial intelligence Computational modeling Adaptive systems Military computing Information analysis Analytical models Mathematical model Computer industry Industrial relations Mechatronics

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A New Method for Handwritten Scene Text Detection in Video

A New Method for Handwritten Scene Text Detection in Video

引用

international Workshop on Frontiers in Handwriting recognition

作者： Palaiahnakote Shivakumara Anjan Dutta Umapada Pal Chew Lim Tan School of Computing National University of Singapore Singapore Computer Vision Center Universitat Authnòma de Barcelona Barcelona Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. The method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. The output of maximum color difference is fed to a K-means algorithm with K = 2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising.

关键词： Pixel Image edge detection Image color analysis Graphics Clustering algorithms Image resolution Measurement

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Text analysis and entity extraction in asymmetric threat response and prediction

Text analysis and entity extraction in asymmetric threat res...

引用

IEEE international conference on Intelligence and Security Informatics, ISI

作者： Erwin Chan Jason Ginsburg Brian Ten Eyck Jerzy Rozenblit Mike Dameron Department of Linguistics University of Arizona Tucson USA Center for Language Research University of Aizu Japan Department of Electrical and Computer Engineering University of Arizona Tucson USA Ephibian Tucson AZ USA

ISBN: (纸本)9781424464449

The Asymmetric Threat Response and Analysis Program (ATRAP) is a software system for intelligence fusion, visualization, reasoning, and prediction. ATRAP consists of a set of tools for annotating and automatically extracting entities and relationships from documents, visualizing this information in relational, geographic, and temporal dimensions, and determining future courses of action of adversaries by creating situational threat templates and applying customized prediction algorithms. In this paper, we first describe the task of analyzing data in intelligence reports, and then provide an overview of major system components: the Text Highlighter tool, the ThoughtSpace TM visualization environment, and the Template Builder and prediction tool. Subsequently, we describe linguistic characteristics of intelligence reports, and describe ATRAP's named entity recognition system.

关键词： Text analysis Data visualization Data mining Information analysis Computer displays pattern analysis Prediction algorithms Predictive models Intelligent systems Military computing

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Adaptive shape prior in graph cut segmentation

Adaptive shape prior in graph cut segmentation

引用

IEEE international conference on Image Processing

作者： Hui Wang Hong Zhang Department of Computing Science University of Alberta Edmonton AB Canada

In this paper, we propose a novel method to adaptively apply shape prior in graph cut segmentation. By incorporating shape priors in an adaptive way, we introduce a robust way to harness shape prior in graph cut segmentation. Since traditional graph cut approaches with shape prior may fail in cases where parameters for shape prior term are not set appropriately, incorporation of shape priors adaptively within this framework mitigates these problems. To address this issue, we propose to adaptively apply shape prior based on a shape probability map, defined to reflect the need of shape prior at each location of an image. We show that the proposed method can be easily applied to existing algorithms of graph cut segmentation with shape prior, such as level set based shape prior method, and star shape prior graph cut. We validate our method in various types of images corrupted by significant noise and intensity inhomogeneities. Convincing results are obtained.

关键词： Shape Image segmentation Pixel conferences Computer vision Equations pattern recognition

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