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检索条件"任意字段=8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012"

共 106 条记录，以下是81-90 订阅

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SOStream: Self organizing density-based clustering over data stream

SOStream: Self organizing density-based clustering over data...

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8th international conference on machine learning and data mining in pattern recognition, mldm 2012

作者： Isaksson, Charlie Dunham, Margaret H. Hahsler, Michael Department of Computer Science and Engineering Southern Methodist University Dallas TX United States

ISBN: (纸本)9783642315367

In this paper we propose a data stream clustering algorithm, called Self Organizing density based clustering over data Stream (SOStream). this algorithm has several novel features. Instead of using a fixed, user defined similarity threshold or a static grid, SOStream detects structure within fast evolving data streams by automatically adapting the threshold for density-based clustering. It also employs a novel cluster updating strategy which is inspired by competitive learning techniques developed for Self Organizing Maps (SOMs). In addition, SOStream has built-in online functionality to support advanced stream clustering operations including merging and fading. this makes SOStream completely online with no separate offline components. Experiments performed on KDD Cup'99 and artificial datasets indicate that SOStream is an effective and superior algorithm in creating clusters of higher purity while having lower space and time requirements compared to previous stream clustering algorithms. © 2012 Springer-Verlag.

关键词： Self organizing maps

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Adult video content detection using machine learning Techniques

Adult video content detection using Machine Learning Techniq...

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8th international conference on Signal Image Technology and Internet Based Systems (SITIS)

作者： Ochoa, Victor M. Torres Yayilgan, Sule Yildirim Cheikh, Faouzi Alaya Gjovik Univ Coll Gjovik Norway

ISBN: (纸本)9780769549118;9781467351522

Automatic adult video detection is a problem of interest to many organizations around the world. the aim is to restrict the easy access of underage youngsters to such potentially harmful material. Most of the existing techniques are mere extensions of image categorization approaches. In this paper we propose a video genre classification technique tuned specifically for adult content detection by considering cinematographic principles. Spatial and temporal simple features are used with machine learning algorithms to perform the classification into two classes: adult and non-offensive video material. Shot duration and camera motion, are the temporal domain features, and skin detection and color histogram are the spatial domain ones. Using two data sets of 7 and 15 hours of video material, our experiments comparing two different SVM classifiers achieved an accuracy of 94.44%.

关键词： machine learning adult-content detection camera motion pattern recognition SVM feature classification

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Outcome prediction for patients with severe traumatic brain injury using permutation entropy analysis of electronic vital signs data

Outcome prediction for patients with severe traumatic brain ...

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8th international conference on machine learning and data mining in pattern recognition, mldm 2012

作者： Kalpakis, Konstantinos Yang, Shiming Hu, Peter F. MacKenzie, Colin F. Stansbury, Lynn G. Stein, Deborah M. Scalea, thomas M. Department of Computer Science and Electrical Engineering University of Maryland Baltimore County 1000 Hilltop Circle Baltimore MD 21250 United States R. Adams Cowley Shock Trauma Center Shock Trauma and Anesthesiology Research Center University of Maryland School of Medicine Baltimore MD 21201 United States

ISBN: (纸本)9783642315367

Permutation entropy is computationally efficient, robust to noise, and effective to measure complexity. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (TBI). Using permutation entropy calculated from early vital signs (initial 10~20% of patient hospital stay time), we built classifiers to predict in-hospital mortality, and mobility measured by 3-month Extended Glasgow Outcome Score (GOSE). Sixty patients with severe TBI produced a skewed dataset that we evaluated for accuracy, sensitivity and specificity. With early vital signs data, the overall prediction accuracy achieved 91.67% for mortality, and 76.67% for 3-month GOSE in testing datasets, using the leave-one-out cross validation. We also applied Receiver Operating Characteristic analysis to compare classifiers built from different learning methods. those results support the applicability of permutation entropy in analyzing the dynamic behavior of biomedical time series for early prediction of mortality and long-term patient outcomes. © 2012 Springer-Verlag.

关键词： Hospitals

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A Knowledge Discovery Methodology For Semantic Categorization of Unstructured Textual Sources

A Knowledge Discovery Methodology For Semantic Categorizatio...

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8th international conference on Signal Image Technology and Internet Based Systems (SITIS)

作者： Toti, Daniele Atzeni, Paolo Polticelli, Fabio Roma Tre Univ Dept Comp Sci & Engn Rome Italy

ISBN: (纸本)9780769549118;9781467351522

We describe a methodology for identifying characterizing terms from a source text or paper and automatically building an ontology around them, with the purpose of semantically categorizing a paper corpus where documents sharing similar subjects may be subsequently clustered together by means of ontology alignment. We first employ a Natural Language Processing pipeline to extract relevant terms from the source text, and then use a combination of a pattern-based and machine-learning approach to establish semantic relationships among those terms, with some user's feedback required in-between. this methodology for discovering characterizing knowledge from textual sources finds its inception as an extension of PRAISED, our abbreviation discovery framework, in order to enhance its resolution capabilities. By moving from a paper-by-paper, mainly syntactical process to a corpus-based, semantic approach, it was in fact possible to overcome earlier limits of the system related to abbreviations whose explanation could not be found within the same paper they were cited in. At the same time, though, the methodology we present is not tied to this specific task, but is instead of relevance for a variety of contexts, and might therefore be used to build a stand-alone system for advanced knowledge extraction and semantic categorization.

关键词： Semantics Stand-Alone System limit system Ontology Information Extraction Abbreviation data mining methodology texts

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An attribute reduction method based on SVM

An attribute reduction method based on SVM

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international conference on Natural Computation (ICNC)

作者： Xiao Tang Zhiwen Mo School of Mathematical Sciences University of Electronic Science and Technology Chengdu China College of Mathematics and Software Science Sichuan Normal University Chengdu China

Recently, support vector machine (SVM), which is based on statistical learning theory emerges as a hot spot in artificial intelligence. It has been widely applied in pattern recognition and many fields. A new attribute reduction method based on SVM is proposed and realized. this paper provides a new way of thinking for attribute reduction.

关键词： Support vector machines Rough sets Approximation methods Educational institutions data mining Classification algorithms

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Automated recognition of human gait pattern using manifold learning algorithm

Automated recognition of human gait pattern using manifold l...

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international conference on Natural Computation (ICNC)

作者： Jianning Wu School of Mathematics and Computer Science Fujian Normal University Fuzhou China

In this paper, we investigated the application of the manifold learning algorithm in gait data analysis for the improvement of the gait classification performance. A manifold learning algorithm such as isometric feature mapping algorithm (ISOMAP) was firstly employed to perform nonlinear feature extraction for initiating the training set, and its effect on a subsequent classification was then tested in combination with learning algorithms such as support vector machines. the gait data including young and elderly participants were analyzed, and the experimental results demonstrated that the generalization performance of ISOMAP-SVM is an evidently improved performance compared to the traditional classifier for recognizing young-elderly gait patterns. Our work suggested that manifold learning algorithm can find the intrinsic low-dimensional manifold embedding in high-dimensional gait data, and obtain the `true' nonlinear gait features associated with human gait function change for improving the gait classification performance. the proposed technique has considerable potential for future clinical applications.

关键词： Manifolds Classification algorithms Algorithm design and analysis Feature extraction Support vector machines Humans machine learning

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Artificial Intelligence and Computational Intelligence 2012

引用

丛书名： Lecture Notes in Computer Science

2012年

作者： Jingsheng Lei Fu Lee Wang Hepu Deng Duoqian Miao

ISBN: (数字)9783642334788

ISBN: (纸本)9783642334771

this volume proceedings contains revised selected papers from the 4th international conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. the total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. the papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

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Advances in Intelligent data Analysis VIII 2009

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丛书名： Lecture Notes in Computer Science

2009年

作者： Niall M. Adams Céline Robardet Arno Siebes Jean-Francois Boulicaut

ISBN: (数字)9783642039157

ISBN: (纸本)9783642039140

this book constitutes the refereed proceedings of the 8th international conference on Intelligent data Analysis, IDA 2009, held in Lyon, France, August 31 September 2, 2009. the 33 revised papers, 18 full oral presentations and 15 poster and short oral presentations, presented were carefully reviewed and selected from almost 80 submissions. All current aspects of this interdisciplinary field are addressed; for example interactive tools to guide and support data analysis in complex scenarios, increasing availability of automatically collected data, tools that intelligently support and assist human analysts, how to control clustering results and isotonic classification trees. In general the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

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BIOADI: A machine learning approach to identifying abbreviations and definitions in biological literature

BIOADI: A machine learning approach to identifying abbreviat...

引用

8th international conference on Bioinformatics: Bioinformatics, InCoB 2009

作者： Kuo, Cheng-Ju Ling, Maurice H.T. Lin, Kuan-Ting Hsu, Chun-Nan Institute of Information Science Academia Sinica Taipei 115 Taiwan Singapore Polytechnic School of Chemical and Life Sciences Singapore Singapore Institute of Biomedical Informatics National Yang-Ming University Taipei 112 Taiwan Department of Zoology University of Melbourne Parkville VIC Australia

Background: To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. the successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools. Results: Our approach to abbreviation recognition (AR) is based on machine-learning, which exploits a novel set of rich features to learn rules from training data. Tested on the AB3P corpus, our system demonstrated a F-score of 89.90% with 95.86% precision at 84.64% recall, higher than the result achieved by the existing best AR performance system. We also annotated a new corpus of 1200 PubMed abstracts which was derived from BioCreative II gene normalization corpus. On our annotated corpus, our system achieved a F-score of 86.20% with 93.52% precision at 79.95% recall, which also outperforms all tested systems. Conclusion: By applying our system to extract all short form-long form pairs from all available PubMed abstracts, we have constructed BIOADI. mining BIOADI reveals many interesting trends of bio-medical research. Besides, we also provide an off-line AR software in the download section on http://***. ***/BIOADI/. © 2012 BioMed Central Ltd .

关键词： Genes

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A stagewise least square loss function for classification

A stagewise least square loss function for classification

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8th SIAM international conference on data mining 2008, Applied Mathematics 130

作者： Yang, Shuang-Hong Hu, Bao-Gang National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9781605603179

this paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex loss function in a stagewise manner. Several benefits are obtained from using the SLS loss function, such as: (i) higher generalization accuracy and better scalability than classical least square loss;(ii) improved performance and robustness than convex loss (e.g., hinge loss of SVM);(iii) computational advantages compared with nonconvex loss (e.g. ramp loss in ψ-learning);(iv) ability to resist myopia of Empirical Risk Minimization and to boost the margin without boosting the complexity of the classifier. In addition, it naturally results in a kernel machine which is as sparse as SVM, yet much faster and simpler to train. A fast online learning algorithm with an integrated sparsification procedure is also provided. Experimental results on several benchmarks confirm the advantages of the proposed approach.

关键词： Least squares approximations

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