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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3282 条记录，以下是2691-2700 订阅

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RETRACTED ARTICLE: Research on rapid detection of total bacteria in juice based on biomimetic pattern recognition and machine vision

RETRACTED ARTICLE: Research on rapid detection of total bact...

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作者： Jin, Shenglang Yin, Yongguang Tourism College Huangshan University Huangshan China School of Biological and Agricultural Engineering Jilin University Changchun China

ISBN: (纸本)9781424455386

In order to develop an automatic and rapid detection method for enumeration of total bacteria in juice, biomimetic pattern recognition and machine vision were employed. The characteristic data, such as shape, texture and color features, were acquired by using the machine vision technology from bacteria images in varieties of juice. Based on multi-weight higher order neuron network, the recognition models were established which can achieve the imitation of human learning, memorizing and judging. By applying the principle of statistics, the detection results of new method showed no difference, compared with the traditional method in apple juice, tomato juice and carrot juice. The new method simplifies experimental preparation and shortens judgment time, especially in sample test on the spot and monitoring production site. Moreover, by using this rapid detection method, total bacteria counts in samples could be accurately enumerated within 1 h, which was much less than 24-48 h by using the traditional method. © 2010 IEEE.

关键词： Computer vision

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Border security: Supplementing human intelligence in a sensor network using sequential pattern mining

Border security: Supplementing human intelligence in a senso...

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2010 3rd international conference on Human-Centric Computing, HumanCom 2010

作者： Kondaveeti, Anirudh Runger, George Rowe, Jeremy Liu, Huan School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781424475704

Sensors are being deployed to improve border security generating enormous collections of data and databases. Unfortunately these sensors can respond to a variety of stimuli, sometimes reacting to meaningful events and sometimes triggered by random events which are considered false alarms. The intent of this project is to supplement human intelligence in a sensor network framework that can assist in filtering and real-time decision making from the large volume of data generated. Our conceptual design of a human-computer system is to use off-line learning to identify the important patterns. The critical real-time system uses the identified patterns from off-line learning in a system that relates the risks of false alarms with the length of patterns and the time interval distributions between sensors in the patterns to allow the human to generate intervention decisions. The human would supplement the computer information with the current threat levels and the available resources for reactions. © 2010 IEEE.

关键词： Sensor networks

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Conceptual mapping of Risk Management to data mining

Conceptual mapping of Risk Management to Data Mining

引用

international conference on Emerging Trends in Engineering and Technology

作者： Johnson, Terence Dept. of Information Technology Padre Conceicao College of Engineering Verna - Goa India

ISBN: (纸本)9780769542461

Risk Management is a logical and systematic method of identifying, analyzing, treating and monitoring the risks involved in any activity or process. The key to successful risk management lies in the ability to tailor a formal risk management process that addresses the complementary needs of the business and its customers. A formal risk management process is a continuous process for systematically addressing risk throughout the product/project life-cycle. Risks can be introduced (or latently reside) at the very earliest stages of the project life-cycle. The ability to identify risks earlier translates into earlier risk removal, at less cost, which promotes higher project success probability. data mining refers to discovery or "mining" of knowledge from large amounts of data. data mining has been described as a confluence of different disciplines primarily database systems, statistics, machine learning and information science. This paper aims to study the conceptual mapping of Risk Management to data mining. A new paradigm has been suggested for Risk Management using the main attributes and key aspects of data mining. © 2010 IEEE.

关键词： data mining

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A new method of data mining based on fuzzy neural network

A new method of Data Mining based on fuzzy neural network

引用

international conference on Advanced Computer Theory and Engineering

作者： Xi, Li Yujie, Zheng Guangxi University for Nationalities College of Mathematics and Computer Science Guang Xi Nanning China GuangXi Economic Management Cadre College School of Computer GuangXi Nanning China

ISBN: (纸本)9781424465408

The fuzzy neural network technology is one of the hot topics of data mining. According to the Max Similarity Rule, this paper sets forth the cross entropy theory with formulae deduction in detail and a new activation function. Compare with the BP algorithm (error back propagation), which based on the error square sum rule and Sigmoid or Hyperbolical function, the classify algorithm based on the cross entropy theory and the new activation function can speed up the learning process and at same time without worry about to put it into the non-convergence state or lose in local small point. The new activation function not only has the value range from 0 to 1, but can also tune up the learning speed by adjusting its slope. So it can improve the algorithm's dynamic performance and make the processing of the FNN get into convergence as soon as possible, which can improve the algorithm's efficiency. In order to identify the user in the Web mining, an idea, which uses the biometric recognition technology, is put forward. At the same time, a method based on the Hidden Markov Model to build an iris identification system is proposed either. The robust of iris matching can be achieved by only depending on the orientation field of the iris and less sensitive to the noise and the distortions of the iris image than the conventional approaches in which need many iris details. This method is more efficient by sampling the pretreatment processes. © 2010 IEEE.

关键词： Websites

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Proceedings of the 2010 international conference of Soft Computing and pattern recognition, SoCPaR 2010

Proceedings of the 2010 International Conference of Soft Com...

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2010 international conference of Soft Computing and pattern recognition, SoCPaR 2010

ISBN: (纸本)9781424478958

The proceedings contain 86 papers. The topics discussed include: iris features extraction using dual-tree complex wavelet transform;fuzzy methods for forensic data analysis;a new weighted rough set framework for imbalance class distribution;multi stereo camera data fusion for fingertip detection in gesture recognition systems;recognition of signed expressions using visually-oriented subunits obtained by an immune-based optimization;3-D object recognition based on SVM and stereo-vision: application in endoscopic imaging;inter-camera color calibration for object re-identification and tracking;improving the accuracy of intrusion detection systems by using the combination of machine learning approaches;mining web videos for video quality assessment;ultra fast fingerprint indexing for embedded system;damageless image hashing using neural network;and classification by means of fuzzy analogy-related proportions - a preliminary report.

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Scaling Up Feature Selection by Means of Democratization

Scaling Up Feature Selection by Means of Democratization

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23rd international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： de Haro-Garcia, Aida Garcia-Pedrajas, Nicolas Univ Cordoba Dept Comp & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9783642130243

The overwhelming amount of data that is available nowadays makes many of the existing machine laming algorithms inapplicable to many real-world problems Two approaches have been used to deal with this problem scaling up data mining algorithms [1] and data reduction Nevertheless. scaling up a certain algorithm is not always feasible. One of the most common methods for data reduction is feature selection. but when we face large problems, the scalability becomes an issue This paper presents a way of removing this difficulty using several rounds of feature selection on subsets of the original dataset, combined using a voting scheme The performance is very good in terms of testing error and storage reduction, while the execution time of the process is decreased very significantly The method is especially efficient when we use feature selection algorithms that are of a high computational cost An extensive comparison in 27 datasets of medium and large sizes front the UCI machine learning Repository and using different classifiers shows the usefulness of our method.

关键词： Feature Selection Instance-based learning Classification Huge Problems Scale-up Divide & Conquer data mining Dimensionality Reduction pattern Discovery Ensembles Genetic Algorithms Relief Algorithms

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learning URL patterns for webpage de-duplication 10

Learning URL patterns for webpage de-duplication

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3rd ACM international conference on Web Search and data mining, WSDM 2010

作者： Koppula, Hema Swetha Leela, Krishna P. Agarwal, Amit Chitrapura, Krishna Prasad Garg, Sachin Sasturkar, Amit Yahoo Labs. Bangalore India *** Bangalore India Yahoo Inc. Sunnyvale CA United States

ISBN: (纸本)9781605588896

Presence of duplicate documents in the World Wide Web adversely affects crawling, indexing and relevance, which are the core building blocks of web search. In this paper, we present a set of techniques to mine rules from URLs and utilize these rules for de-duplication using just URL strings without fetching the content explicitly. Our technique is composed of mining the crawl logs and utilizing clusters of similar pages to extract transformation rules, which are used to normalize URLs belonging to each cluster. Preserving each mined rule for de-duplication is not efficient due to the large number of such rules. We present a machine learning technique to generalize the set of rules, which reduces the resource footprint to be usable at web-scale. The rule extraction techniques are robust against web-site specific URL conventions. We compare the precision and scalability of our approach with recent efforts in using URLs for de-duplication. Experimental results demonstrate that our approach achieves 2 times more reduction in duplicates with only half the rules compared to the most recent previous approach. Scalability of the framework is demonstrated by performing a large scale evaluation on a set of 3 Billion URLs, implemented using the MapReduce framework. Copyright 2010 ACM.

关键词： MapReduce

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Pairwise interaction tensor factorization for personalized tag recommendation 10

Pairwise interaction tensor factorization for personalized t...

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3rd ACM international conference on Web Search and data mining, WSDM 2010

作者： Rendle, Steffen Schmidt-Thieme, Lars Department of Reasoning for Intelligence Institute of Scientific and Industrial Research Osaka University Osaka Japan Institute for Computer Science University of Hildesheim Hildesheim Germany Machine Learning Lab. University of Hildesheim Germany

ISBN: (纸本)9781605588896

Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in runtime and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation. Copyright 2010 ACM.

关键词： Recommender systems

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Coupled semi-supervised learning for information extraction 10

Coupled semi-supervised learning for information extraction

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3rd ACM international conference on Web Search and data mining, WSDM 2010

作者： Carlson, Andrew Betteridge, Justin Wang, Richard C. Hruschka Jr., Estevam R. Mitchell, Tom M. Schoool of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States Federal University of Sao Carlos Sao Carlos SP Brazil

ISBN: (纸本)9781605588896

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result. Copyright 2010 ACM.

关键词： Supervised learning

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An initialization method for fuzzy c-means algorithm using subtractive clustering

An initialization method for fuzzy c-means algorithm using s...

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international conference on Intelligent Network and Intelligent Systems

作者： Yang, Qing Zhang, Dongxu Tian, Feng School of Information Science and Engineering Shenyang Ligong University Shenyang 110159 China College of Optical and Electronical Information Changchun University of Science and Technology Changchun 130022 China

ISBN: (纸本)9780769542492

In clustering methods, the estimation of the optimal number of clusters is significant for subsequent analysis. As a simple clustering method, the fuzzy c-means algorithm (FCM) has been widely discussed and applied in pattern recognition and machine learning. However, the FCM could not guarantee unique clustering result because initial cluster number is chosen randomly. As the number of clusters is randomly chosen, the iterative amount is large and the result of the classification is unstable. An initialization method for FCM algorithm using subtractive clustering is presented in this paper. The experiments show that the modified algorithm can improve the speed, and reduce the iterative amount. At the same time, this method can make the results of the classification more stable and have higher precision. © 2010 IEEE.

关键词： pattern recognition

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