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IEEE International Conference on Image Processing

作者： Yanfeng Gu Qingwang Wang Pigang Liu Deshan Zuo Sch. of Electron. & Inf. Eng. Harbin Inst. of Technol. Harbin China

ISBN: (纸本)9781479957521

In the past decade, with the development of kernel-based machine learning, many different multiple kernel learning (MKL) methods were proposed which focus on selecting the pivotal kernel to be preserved and confirming the optimal kernel combination. In this paper, we address the question mentioned above by using subspace projection method and put forward a linear discriminant based MKL (LDMKL) algorithm. LDMKL algorithm reduces the computational burden and keeps the excellent property of MKL in terms of good classification accuracy by finding the optimal projective direction which makes the intraclass scatter minimum and interclass scatter maximum instead of the time-consuming search for optimal kernel combination. Experimental results indicate that LDMKL algorithm provides the best performances among several the state-of-the-art algorithms while demonstrating satisfactory computational efficiency.

关键词： image classification learning (artificial intelligence) LDMKL algorithm interclass scatter maximum intraclass scatter minimum kernel-based machine learning linear discriminant multiple kernel learning method multispectral image classification subspace projection method Accuracy Classification algorithms Image classification Kernel Remote sensing Support vector machines Training Classification linear discriminant analysis (LDA) multiple kernel learning (MKL) spectral images support vector machine (SVM) Image classification Support Vector Network sorting algorithm Kernel Seeds remote sensing Functional training Linear discriminant analysis multi-spectral image Discriminant

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LARGE MARGIN FILTERING FOR SIGNAL SEQUENCE LABELING

LARGE MARGIN FILTERING FOR SIGNAL SEQUENCE LABELING

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Remi Flamary Benjamin Labbe Alain Rakotomamonjy LITIS EA 4108 INSA-Universite de Rouen 76801 Saint-Etienne-du-Rouvray France

ISBN: (纸本)9781424442959

Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and to apply a classification algorithm on the filtered samples. We propose in this paper to jointly learn the filter with the classifier leading to a large margin filtering for classification. This method allows to learn the optimal cutoff frequency and phase of the filter that may be different from zero. Two methods are proposed and tested on a toy dataset and on a real life BCI dataset from BCI Competition III.

关键词： Filtering SVM BCI Sequence Labeling Filtration Support Vector Network Filters sorting algorithm signals Cutoff frequency LARGE gene Classifiers

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Whole-House Non-Intrusive Appliance Load Monitoring via Multi-Label Classification

Whole-House Non-Intrusive Appliance Load Monitoring via Mult...

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International Joint Conference on Neural Networks

作者： Ding Li Scott Dick Department of Electrical and Computer Engineering University of Alberta

ISBN: (纸本)9781509006212

Non-intrusive appliance load monitoring is a technique to help power companies monitor and analyze residential energy usage. Aggregated power load measurements are disaggregated into individual appliance loads by examining the appliance-specific power consumption characteristics. This data can then be used to modify consumer behaviors via detailed billing data and/or demand-pricing tariffs. A number of advances in the field have been reported in the past two decades, many of which apply machine learning algorithms. However, these algorithms only assign one label to an example, which is a poor match to the NIALM problem, meaning elaborate encodings or classifier ensembles are needed for NIALM. A more elegant solution would be to use algorithms that assign multiple labels to a single example. These multi-label classification algorithms have received very little attention in this field to date. We conduct an experimental investigation of four multi-label classification algorithms for non-intrusive monitoring, and find that they are superior to all prior reported results on a real-world household dataset.

关键词： Non-intrusive monitoring Load disaggregation Feature extraction Multi-label classification load monitoring Feature extraction sorting algorithm Classifier Ensembles Power consumption

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Arithmetic Means of Accuracies: A Classifier Performance Measurement for Imbalanced Data Set

Arithmetic Means of Accuracies: A Classifier Performance Mea...

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2010 International Conference on Audio,Language and Image Processing(2010年音频、语言与图像处理国际会议 ICALIP 2010)

作者： Ivanna K. Timotius Shaou-Gang Miaou Department of Electronic Engineering Satya Wacana Christian University Salatiga Indonesia Departm Department of Electronic Engineering Chung Yuan Christian University Chung Li Taiwan

ISBN: (纸本)9781424458561

Classifier performance measurement is essential in the development and analysis of classification algorithms. This paper proposes a new measurement approach that can be used generally for the balanced and imbalanced data set, can reflect the random guessing behavior perfectly, and can be used easily in cost-sensitive classification and multiple-class classification.

关键词： Dataset sorting algorithm Performance metrics arithmetical mean Classifiers ASSAY SCALE arithmetic Phyla Guess

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CLASSIFYING THE CANADIAN BOREAL FOREST'S STRUCTURE USING MULTI-MODAL REMOTE SENSING

CLASSIFYING THE CANADIAN BOREAL FOREST'S STRUCTURE USING MUL...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Michael L. Benson Leland E. Pierce Kathleen M. Bergen Kamal Sarabandi The University of Michigan The Radiation Laboratory EECS Dept. The University of Michigan School of Natural Resources and the Environment

ISBN: (纸本)9781467311601

One of the most fundamental new technical challenges of a DESDynI-R space-borne mission is the fusion of the several sensor modalities, including the onboard SAR and external LiDAR and Optical sensors in order to accurately estimate desired 3D vegetation structures and biomass parameters in areas where the sensors overlap. The objective of this paper is to use measured datasets in conjunction with our sensor models to develop a classification algorithm that fuses multi-modal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height.

关键词： SAR LiDAR Landsat Forest structure Classification Fractal tree VIR Fusion BOREAS DESDynI sorting algorithm Sensors remote sensing optical sensors Forest forest structure Fusion reactions FUSION fusion (melting) Remote Sensing Technologies

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Joint Gender-, Tone-, Vowel- Classification Via Novel Hierarchical Classification for Annotation of Monosyllabic Mandarin Word Tokens

Joint Gender-, Tone-, Vowel- Classification Via Novel Hierar...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Saurabh Garg Ghassan Hamarneh Allard Jongman Joan Sereno Yue Wang Pacific Parkinson's Research Centre University of British Columbia Canada Medical Image Analysis Lab Simon Fraser University (SFU) Canada KU Phonetics and Psycholinguistics Lab Department of Linguistics University of Kansas (KU) USA Language and Brain Lab Department of Linguistics Simon Fraser University Canada

ISBN: (纸本)9781538646595

The automatic annotation of Mandarin monosyllabic audio word tokens remains an important yet challenging issue in phonetics research. In this work, we address this annotation task via a novel subcategories-classification framework that not only performs word identification via the joint classifications of vowel and tone subcategories, but also performs gender discrimination of the speaker, which stands in contrast to previously proposed methods for Mandarin speech that focused only on tone-, vowel-, or gender- classification. We also propose a novel hierarchical classification algorithm to boost overall classification performance. Extensive experimental results show that our approach yielded superior performance in both cases of adequate and very limited training data. When trained using data from only one female and one male speaker, our approach also yielded the best classification accuracy in all subcategories of the token annotation problem, achieving an Fl-score of 0.742 as opposed to 0.705 as achieved by the second competing approach.

关键词： monosyllabic Mandarin tokens phonetics hierarchical classification label fusion multi-class S VMs phonetics Speech Sounds ANNOTATIONS Joints Loudspeakers Sex Discrimination token Taxonomy sorting algorithm

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Comparison of Detection and Classification algorithms Using Boolean and Fuzzy Techniques

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ADVANCES IN FUZZY SYSTEMS 2012年第unknown期2012卷 406204.1-406204.10页

作者： Dixit, Rahul Singh, Harpreet Wayne State Univ Dept Engn & Comp Engn Detroit MI 48202 USA Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

Modern military ranging, tracking, and classification systems are capable of generating large quantities of data. Conventional "brute-force" computational techniques, even with Moore's law for processors, present a prohibitive computational challenge, and often, the system either fails to "lock onto" a target of interest within the available duty cycle, or the data stream is simply discarded because the system runs out of processing power or time. In searching for high-fidelity convergence, researchers have experimented with various reduction techniques, often using logic diagrams to make inferences from related signal data. Conventional Boolean and fuzzy logic systems generate a very large number of rules, which often are difficult to handle due to limitations in the processors. Published research has shown that reasonable approximations of the target are preferred over incomplete computations. This paper gives a figure of merit for comparing various logic analysis methods and presents results for a hypothetical target classification scenario. Novel multiquantization Boolean approaches also reduce the complexity of these multivariate analyses, making it possible to better use the available data to approximate target classification. This paper shows how such preprocessing can reasonably preserve result confidence and compares the results between Boolean, multi-quantization Boolean, and fuzzy techniques.

关键词： Fuzzy multivariate analysis Quality factor PROCESSOR sorting algorithm Taxonomy processing power Targets logic diagram LARGE QUANTITIES data flow fuzzy logic system DUTY CYCLE

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Automated Cardiac Disease Diagnosis Using Support Vector Machine

Automated Cardiac Disease Diagnosis Using Support Vector Mac...

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International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA)

作者： Samir S. Yadav Shivajirao M. Jadhav Rohit G. Bonde Shivani T. Chaudhari Dr. Babasaheb Ambedkar Technological University Raigad India

ISBN: (数字)9781728123394

ISBN: (纸本)9781728123400

This research is intended for the creation of an automatic cardiovascular disease detection system with pre-marked SPECT (Single photon emission computed tomography)data for training the program and for subsequently supplying it with unclassified SPECT data for classification as an indication of probable heart disease or not. We used a well-known and widely used classification algorithm named Support Vector Machines (SVM) for the purposes of this classification. In other terms, you instruct the machine; the system learns from the inputs you give. When the training is over, you can provide the system with undefined SPECT data and it is immediately identified by the system Clearly, the accuracy of this classification would depend on the precision of the pictures of the training system, the support vector machine parameters, the sophistication and many more variables that are discussed later. The project is of considerable significance in today's world, particularly if businesses offer a million-dollarreward to anyone capable of building automated systems detecting nearly every Star Wars (such as transcoder) disease.

关键词： aNN Support Vector Machines Linear Kernel SPECT Gaussian Kernel SVM Support Vector Network single photon emission computed tomography Heart Diseases Taxonomy Disease diagnosis Detection systems sorting algorithm Training Photon emission Business

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A novel gender classification method based on MapReduce

A novel gender classification method based on MapReduce

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Tong Cui Haifeng Zhao Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781479983544

A novel parallelize gender recognition method with MapReduce is presented, which successfully comprise several machine leaning algorithms which are employed for gender recognition. The mass of face sample images are gathered and separated as train dataset and test dataset, and Local Binary Pattern (LBP) features are extracted when those sample sets are pre-processed and made ready for following operations. And Principle Component Analysis (PCA) is applied to train dataset to extract the most distinguishing features. Three classification algorithms: Support Vector Machine(SVM), k-Nearest Neighborhood (k-NN) and Adaboost are implemented and compared to determine the most suitable and successful algorithm for gender parallelize machine learning (GPML). To achieve the shortest execution time, we propose to apply GPML with MapReduce to avoid parallelizing above three algorithms while also improving their scalability to big datasets. The results show that this method reduces the training computational complexity significantly when the number of computing nodes increases while gaining better speedup rates and extending performance than those on parallelize Adaboost.

关键词： face recognition feature extraction image classification learning (artificial intelligence) parallel processing principal component analysis support vector machines Adaboost GPML LBP features MapReduce PCA SVM classification algorithm face sample images gender classification method gender parallelize machine learning k-nearest neighborhood local binary pattern feature extraction machine leaning algorithms parallelized gender recognition method principle component analysis support vector machine test dataset train dataset training computational complexity gender recognition parallel meta-learning Support Vector Network principal components analysis sorting algorithm Image classification PARALLEL PROCESSING (COMPUTERS) Facial Recognition Passive Cutaneous Anaphylaxis Feature extraction Dataset Gender ADABOOST execution time Classification Methods

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Towards Using Subpattern Distributions in Social Network Analysis

Towards Using Subpattern Distributions in Social Network Ana...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

作者： Benjamin Cabrera University Duisburg-Essen

ISBN: (纸本)9781509028474

Determining the frequencies and the distribution of small subgraph patterns in a large input graph is an important part of many graph based mining tasks such as Frequent Subgraph Mining (FSM) and Motif Detection. Due to the exponential number of such graph patterns the interpretation of the mining results is mostly limited to finding unexpectedly frequent patterns, and in general identifying few particularly interesting patterns to then understand their function in the network. However, the full distribution of patterns itself encodes much more information about the underlying graph. Looking at this pattern distribution could be of particular interest in Social Network Analysis because social networks seem to have more random noise and less hard structural constraints compared to other types of graphs. This makes it unlikely to find meaning in only the most frequent patterns. In this paper we contribute in two ways to the usage of pattern distributions in networks analysis. First, we introduce two different sampling-based algorithms for computing the mentioned pattern distributions. Second we sketch two original ideas which can be used to harness the information in pattern distributions for gaining insights on global and local properties of Social Networks.

关键词： Social network services Image edge detection Approximation algorithms Databases Context Classification algorithms Clustering algorithms distribution (property) Social network services Edge detection sorting algorithm Clustering algorithms Social network analysis Approximation algorithms Network analysis Database Social Networks subschema Line graph

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