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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005"

共 135 条记录，以下是31-40 订阅

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A Privacy-preserving Crowd Movement Analysis by k-member Clustering of Face Images 4

A Privacy-preserving Crowd Movement Analysis by <i>k</i>-mem...

引用

4th international conference on Informatics, Electronics & Vision (lCIEV)

作者： Honda, Katsuhiro Omori, Masahiro Ubukata, Seiki Notsu, Akira Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan

ISBN: (纸本)9781467369022

Crowd movement analysis is an important issue in social design. this paper studies an machine learning approach to crowd movement estimation through face image recognition. Although high performance face recognition is a powerful tool in individual authentication with surveillance camera images in public spaces, utilization of personal information is often hesitated under fear of privacy violation. In this paper, a privacy preserving framework for crowd movement analysis is proposed considering k-anonymization of face image features. k-anonymity is a quantitative measure of secureness in data mining and is expected to enhance the utility of personal information. An experimental result demonstrates the applicability of the secure framework in capturing crowd movement characteristics even if individual features are k-aonymized so that each individual is not distinguishable from others k - 1 ones.

关键词： cameras data mining data privacy face recognition learning (artificial intelligence) parallel processing high performance face image recognition k-anonymization k-member clustering machine learning approach privacy violation privacy-preserving crowd movement analysis surveillance camera image Cameras data privacy Face Face recognition Image recognition Logic gates pattern matching data privacy Facial recognition Cameras data mining Image recognition Face Face pattern matching Logic gates PARALLEL PROCESSING (COMPUTERS) Movements

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Applying Clustering Analysis to Heterogeneous data Using Similarity Matrix Fusion (SMF) 11th

Applying Clustering Analysis to Heterogeneous Data Using Sim...

引用

11th international conference on machine learning and data mining (mldm)

作者： Mojahed, Aalaa Bettencourt-Silva, Joao H. Wang, Wenjia de la Iglesia, Beatriz Univ E Anglia Norwich NR4 7TJ Norfolk England

ISBN: (纸本)9783319210247;9783319210230

We define a heterogeneous dataset as a set of complex objects, that is, those defined by several data types including structured data, images, free text or time series. We envisage this could be extensible to other data types. there are currently research gaps in how to deal with such complex data. In our previous work, we have proposed an intermediary fusion approach called SMF which produces a pairwise matrix of distances between heterogeneous objects by fusing the distances between the individual data types. More precisely, SMF aggregates partial distances that we compute separately from each data type, taking into consideration uncertainty. Consequently, a single fused distance matrix is produced that can be used to produce a clustering using a standard clustering algorithm. In this paper we extend the practical work by evaluating SMF using the k-means algorithm to cluster heterogeneous data. We used a dataset of prostate cancer patients where objects are described by two basic data types, namely: structured and time-series data. We assess the results of clustering using external validation on multiple possible classifications of our patients. the result shows that the SMF approach can improved the clustering configuration when compared with clustering on an individual data type.

关键词： Heterogeneous data Big data Distance measure Intermediate data fusion Clustering Uncertainty

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An exact graph edit distance algorithm for solving pattern recognition problems 4

An exact graph edit distance algorithm for solving pattern r...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Abu-Aisheh, Zeina Raveaux, Romain Ramel, Jean-Yves Martineau, Patrick Université François Rabelais Tours37200 France

ISBN: (纸本)9789897580765

Graph edit distance is an error tolerant matching technique emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning and data mining;it represents the minimum-cost sequence of basic edit operations to transform one graph into another by means of insertion, deletion and substitution of vertices and/or edges. A widely used method for exact graph edit distance computation is based on the A∗ algorithm. To overcome its high memory load while traversing the search tree for storing pending solutions to be explored, we propose a depth-first graph edit distance algorithm which requires less memory and searching time. An evaluation of all possible solutions is performed without explicitly enumerating them all. Candidates are discarded using an upper and lower bounds strategy. A solid experimental study is proposed;experiments on a publicly available database empirically demonstrated that our approach is better than the A∗ graph edit distance computation in terms of speed, accuracy and classification rate.

关键词： Classification (of information)

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Modern machine learning techniques and their applications

Modern machine learning techniques and their applications

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作者： Ivanović, Mirjana Radovanović, Miloš Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Novi Sad Serbia

ISBN: (纸本)9781138028302

During the last several decades machine learning (ML) became a mainstay of information technology. One view of ML is that it represents the science of finding patterns and making predictions from data, with the goal of creating systems that learn from experience. ML borders with multivariate statistics, data mining, pattern recognition, and advanced/predictive analytics. Increasing amounts of available data suggest that ML will become an even more pervasive component of technological progress. ML techniques usually belong to one of the main learning styles: supervised, unsupervised, semi-supervised, and reinforcement learning. Relevant methods include decision trees, nearest neighbor techniques, partitional/ hierarchical/density-based clustering, Bayesian approaches, kernel methods, neural networks, and deep learning. the first part of our paper will give an overview of most important ML techniques. We will outline common problems: the bias-variance tradeoff, under/over-fitting, high dimensionality, and big data. the second part will highlight ML applications in business and telecommunications. © 2015 Taylor & Francis Group, London.

关键词： Bayesian networks

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4th international Visual Informatics conference, IVIC 2015

4th International Visual Informatics Conference, IVIC 2015

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4th international Visual Informatics conference, IVIC 2015

ISBN: (纸本)9783319259383

the proceedings contain 45 papers. the special focus in this conference is on Visualisation, Big data, machine learning and Computer Vision. the topics include: Visual information framework for medical family tree data;determining number of clusters using firefly algorithm with cluster merging for text clustering;design solution for complexities of big data interfaces;fuzzy-based shapelets for mining climate change time series patterns;time series analysis and forecasting of dengue using open data;comparative analysis of different versions of association rule mining algorithm on AWS-EC2;visualisation of trend pattern migrations in social networks;initial user requirement analysis for waterbodies data visualization;developmental analysis of a markerless hybrid tracking technique for mobile augmented reality systems;guideline for designing an effective serious game by using cultural-based game design model;evaluation of wearable device for the elderly;violence recognition using harmonic mean of distances and relational velocity with K-nearest neighbour classifier;familiarity in team-based online games;a membrane computing model for generation of picture arrays;score level fusion scheme in hybrid multibiometric system;a critical comparison of fingerprint fuzzy vault techniques;a comparative study of computer aided system preoperative planning for high tibial osteotomy;a comparison of multi-label feature selection methods using the algorithm adaptation approach;an efficient multi join query optimization for relational database management system using two phase artificial bess colony algorithm;BoVW model for animal recognition;objects contributions towards image annotation and optimized-memory map-reduce algorithm for mobile learning.

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A Robust One-Class Classification Model with Interval-Valued data Based on Belief Functions and Minimax Strategy

A Robust One-Class Classification Model with Interval-Valued...

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10th international conference on machine learning and data mining (mldm)

作者： Utkin, Lev V. Zhuk, Yulia A. Chekh, Anatoly I. St Petersburg State Forest Tech Univ Dept Control Automat & Syst Anal St Petersburg Russia Saint Petersburg State Elect Univ Dept Comp Sci St Petersburg Russia

ISBN: (纸本)9783319089799;9783319089782

A robust model for solving the one-class classification problem by interval-valued training data is proposed in the paper. It is based on using Dempster-Shafer theory for getting the lower and upper risk measures. the minimax or pessimistic strategy is applied to state an optimization problem in the framework of the modified support vector machine (SVM). the algorithm for computing optimal parameters of the classification function stems from extreme points of the convex polytope produced by the interval-valued elements of a training set. It is shown that the hard non-linear optimization problem is reduced to a finite number of standard SVMs.

关键词： novelty detection support vector machine kernel interval-valued data minimax strategy quadratic programming Dempster-Shafer theory

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ACCD: Associative Classification over Concept-Drifting data Streams

ACCD: Associative Classification over Concept-Drifting Data ...

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10th international conference on machine learning and data mining (mldm)

作者： Waiyamai, Kitsana Kangkachit, thanapat Saengthongloun, Bordin Rakthanmanon, thanawin Kasetsart Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9783319089799;9783319089782

Associative classification has shown good result over many classification techniques on static datasets. However, little work has been done on associative classification over data streams. Different from data in traditional static databases, data streams typically arrive continuously and unboundedly with occasionally changing data distribution known as concept drift. In this paper, we propose a new Associative Classification over Concept-Drifting data streams, ACCD. ACCD is able to accurately detect concept drift in data streams and reduce its effect by using an ensemble of classifiers. A mechanism for statistical accuracy bounds estimation is used for supporting concept-drift detection. With this mechanism, accuracy recovering time is decreased and a situation where ensemble of classifiers drops accuracy is avoided. Compared to AC-DS (first technique on associative classification algorithm over data streams), AUEH (Accuracy updated ensemble with Hoeffding tree) and VFDT(Very Fast Decision Trees) on 4 real-world data stream datasets, ACCD exhibits the best performance in terms of accuracy.

关键词： concept drift changing environments data streams classification associative classification multiple class-sssociation rules

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An assessment method of the data similarity considering the shape differences of objects

An assessment method of the data similarity considering the ...

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4th international conference on Frontiers of Manufacturing Science and Measuring Technology, ICFMM 2014

作者： Li, Zhong Li, Guo Ping Liu, Yang Yang School of Electrical and Electronic Engineering North China Electric Power University Baoding 071003 China

ISBN: (纸本)9783038351924

the measure of similarity is an essential basic work in the field of pattern recognition, machine learning and data mining, etc. the shape similarity distance (SSD) which considering vector difference factors in the calculation of similarity, is an amendment of the classical distance. Based on the classical Euclidean distance, Manhattan distance and the SSD, this paper completed the clustering on the multiple datasets that contained shape features data of objects. the experimental results indicated that compared with the classical distances, the SSD can consider the characteristics of shape of theses objects to estimate their similarity. © (2014) Trans Tech Publications, Switzerland.

关键词： data mining

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Research on database and Information Technology in the Decision Tree of Communication Construction Scheme 4

Research on Database and Information Technology in the Decis...

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4th international conference on Automation, Communication, Architectonics and Materials (ACAM)

作者： Peng, Yongjun Peng, Yang Liu, Cifang Acad Natl Def Informat Wuhan 430010 Peoples R China Hubei Transportat Res Inst Wuhan 430030 Peoples R China

ISBN: (纸本)9783038352891

With the development of database technology as well as the widespread application of database management system, our capabilities of both generating and collecting data have been increasing rapidly. In addition, popular use of the World Wide Web as a global information system has flooded us with a tremendous amount of data and information. this explosive growth in stored or transient data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. the data mining technology brought forward. data mining, also popularly referred to knowledge discovery from data (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories, or data streams. data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. database and Information Technology in the Decision Tree of is very important for the military. the data mining had been applied and studied in these years, and it has been applied in many domains successfully, such as business, finance and medical treatment. However, little is applied in communication construction scheme.

关键词： Decision tree data mining KDD

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A study on mammography computer aided diagnosis system using machine learning methods 4

A study on mammography computer aided diagnosis system using...

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IET Chennai 4th international conference on Sustainable Energy and Intelligent Systems, SEISCON 2013

作者： Dinakaran, K. Sivakrithika, V. Department of CSE RMD Engineering College India Department of CSE TRP Engineering College India

ISBN: (纸本)9781785610301

Early diagnosis is an important aspect of successful treatment for breast cancer. Mammogram is the most reliable imaging technique available. It is a challenging task for radiologists to detect the abnormalities in the mammograms. Computing helps the radiologists in diagnosing the abnormalities in the mammogram. Computer Aided Diagnosis System involves computerized biomedical image analysis to classify the mammography into benign or malign. In a decade of research work number of algorithms had been proposed to classify the image that employ data mining techniques, image processing methods, machine learning methods and pattern recognition. In this paper such algorithms in previous research work is studied and their performance is discussed.

关键词： Computer aided diagnosis

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