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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"

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

Machine Learning and Data Mining in Pattern Recognition - 8t...

引用

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

ISBN: (纸本)9783642315367

the proceedings contain 51 papers. the topics discussed include: Bayesian approach to the concept drift in the pattern recognition problems;transductive relational classification in the co-training paradigm;generalized nonlinear classification model based on cross-oriented choquet integral;reduction of distance computations in selection of pivot elements for balanced GHT structure;hot deck methods for imputing missing data: the effects of limiting donor usage;a new approach for association rule mining and bi-clustering using formal concept analysis;selecting classification algorithms with active testing;unsupervised grammar inference using the minimum description length principle;how many trees in a random forest?;constructing target concept in multiple instance learning using maximum partial entropy;and a new learning structure heuristic of Bayesian networks from data.

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A Survey of data mining Techniques for Digital Forensic Analysis 16

A Survey of Data Mining Techniques for Digital Forensic Anal...

引用

16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging data and Industry 4.0, EDI40 2025

作者： Abdallah, Emad E. Elsoud, Esraa Abu Abdallah, Alaa E. Department of Information Technology Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa 13133 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa 13133 Jordan

Analyzing digital devices to generate digital evidence relevant to incidents is essential in modern digital investigations. machine learning models and pattern recognition capabilities can be used in forensic analysis to uncover hidden evidence in digital objects that would have been missed if performed manually, especially given the exponential growth in textual sources and digital devices used in the forensic investigation process over the years. Text mining, a subset of data mining, has played an important role in developing effective digital forensic techniques. this survey provides a comprehensive overview of digital forensic analysis using data mining techniques. It presents the critical role of data mining in developing the digital forensics process and addressing the challenges posed by increasing data complexity. © 2025 Elsevier B.V.. All rights reserved.

关键词： Classification Clustering data mining Digital Forensics Feature selection machine learning

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machine learning and data mining in pattern recognition 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Petra Perner

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

this book constitutes the refereed proceedings of the 8th international conference, mldm 2012, held in Berlin, Germany in July 2012. the 51 revised full papers presented were carefully reviewed and selected from 212 submissions. the topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.

关键词： Artificial Intelligence Mathematical Logic and Formal Languages database Management data mining and Knowledge Discovery pattern recognition Image Processing and Computer Vision

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Generalized Hand Gesture recognition for Wearable Devices in IoT: Application and Implementation Challenges 12th

Generalized Hand Gesture Recognition for Wearable Devices in...

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12th international conference on machine learning and data mining (mldm)

作者： Iyer, Darshan Mohammad, Fahim Guo, Yuan Al Safadi, Ebrahim Smiley, Benjamin J. Liang, Zhiqiang Jain, Nilesh K. Intel Corp Hillsboro OR 97124 USA

ISBN: (纸本)9783319419206;9783319419190

the proliferation of low power and low cost continuous sensing technology is enabling new and innovative applications in wearables and Internet of things (IoT). At the same time, new applications are creating challenges to maintain real-time response in a resource-constrained device, while maintaining an acceptable performance. In this paper, we describe an IMU (Inertial Measurement Unit) sensor-based generalized hand gesture recognition system, its applications, and the challenges involved in implementing the algorithm in a resource-constrained device. We have implemented a simple algorithm for gesture spotting that substantially reduces the false positives. the gesture recognition model was built using the data collected from 52 unique subjects. the model was mapped onto Intel (R) Quark (TM) SE pattern Matching Engine, and field-tested using 8 additional subjects achieving 92% performance.

关键词： Gesture recognition machine learning pattern recognition Feature engineering Wearable device Internet of things

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Bayesian approach to the concept drift in the pattern recognition problems

Bayesian approach to the concept drift in the pattern recogn...

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

作者： Turkov, Pavel Krasotkina, Olga Mottl, Vadim Tula State University 92 Lenina Ave. Tula 300600 Russia Computing Center Russian Academy of Science 40 Vavilov St. Moscow 119333 Russia

ISBN: (纸本)9783642315367

We can face with the pattern recognition problems where the influence of hidden context leads to more or less radical changes in the target concept. this paper proposes the mathematical and algorithmic framework for the concept drift in the pattern recognition problems. the probabilistic basis described in this paper is based on the Bayesian approach to the estimation of decision rule parameters. the pattern recognition procedure derived from this approach uses the general principle of the dynamic programming and has linear computational complexity in contrast to polynomial computational complexity in general kind of pattern recognition procedure. © 2012 Springer-Verlag.

关键词： Computational complexity

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Computer recognition of facial expressions of emotion

Computer recognition of facial expressions of emotion

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

作者： PiĄtkowska, Ewa Martyna, Jerzy Institute of Applied Computer Science Jagiellonian University Reymonta 4 30-059 Cracow Poland Institute of Computer Science Faculty of Mathematics and Computer Science Jagiellonian University Prof. S. Lojasiewicza 6 30-348 Cracow Poland

ISBN: (纸本)9783642315367

In this paper, we study the computer recognition of emotions involved in facial expressions. We propose a recognition system based on a support vector machine (SVM) system as a classifier for detecting of spontaneous emotions. Using a face detection algorithm we created theface representation. then, the face texture is encoded with Local Binary patterns (LBP) and used as a feature set in emotion recognition. the presented classifier can be useful a.o. for aggression classification and automatic emotion exploration. © 2012 Springer-Verlag.

关键词： Local binary pattern

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Constructing target concept in multiple instance learning using maximum partial entropy

Constructing target concept in multiple instance learning us...

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

作者： Xu, Tao Chiu, David Gondra, Iker School of Computer Science University of Guelph ON Canada Mathematics Statistics Computer Science St. Francis Xavier University NS Canada

ISBN: (纸本)9783642315367

Multiple instance learning, when instances are grouped into bags, concerns learning of a target concept from the bags without reference to their instances. In this paper, we advance the problem with a novel method based on computing the partial entropy involving only the positive bags using a partial probability scheme in the attribute subspace. the evaluation highlights what could be obtained if information only from the positive bags is used, while the contributions from the negative bags are identified. the proposed method attempts to relax the dependency on the distribution of the whole probability of training data, but focus only on the selected subspace. Experimental evaluation explores the effectiveness of using maximum partial entropy in evaluating the merits between the positive and negative bags in the learning. © 2012 Springer-Verlag.

关键词： pattern recognition

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A new learning strategy of general BAMs

A new learning strategy of general BAMs

引用

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

作者： Hoa, Nong thi Duy, Bui the Human Machine Interaction Laboratory Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9783642315367

Bi-directional Associative Memory (BAM) is an artificial neural network that consists of two Hopfield networks. the most important advantage of BAM is the ability to recall a stored pattern from a noisy input, which depends on learning process. Between two learning types of iterative learning and non-iterative learning, the former allows better noise tolerance than the latter. However, interactive learning BAMs take longer to learn. In this paper, we propose a new learning strategy that assures our BAM converges in all states, which means that our BAM recalls perfectly all learning pairs. Moreover, our BAM learns faster, more flexibility and tolerates noise better. In order to prove the effectiveness of the model, we have compared our model to existing ones by theory and by experiments. © 2012 Springer-Verlag.

关键词： Hopfield neural networks

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A pattern recognition system for malicious PDF files detection

A pattern recognition system for malicious PDF files detecti...

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

作者： Maiorca, Davide Giacinto, Giorgio Corona, Igino University of Cagliari Piazza d'Armi 09123 Cagliari Italy

ISBN: (纸本)9783642315367

Malicious PDF files have been used to harm computer security during the past two-three years, and modern antivirus are proving to be not completely effective against this kind of threat. In this paper an innovative technique, which combines a feature extractor module strongly related to the structure of PDF files and an effective classifier, is presented. this system has proven to be more effective than other state-of-the-art research tools for malicious PDF detection, as well as than most of antivirus in commerce. Moreover, its flexibility allows adopting it either as a stand-alone tool or as plug-in to improve the performance of an already installed antivirus. © 2012 Springer-Verlag.

关键词： pattern recognition

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Integrating Weight with Ensemble to Handle Changes in Class Distribution

Integrating Weight with Ensemble to Handle Changes in Class ...

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

作者： Limsetto, Nachai Waiyamai, Kitsana Kasetsart Univ Fac Engn Dept Comp Engn Bangkok 10900 Thailand

ISBN: (纸本)9783319089799;9783319089782

Concept drift can be considered as a distribution mismatch problem where class distribution changes as a time passes. this problem is commonly found in classification task of data mining. Among the proposed solutions, the cost-based Class Distribution Estimation (CDE) shows the best performance in coping with difference in class distribution between train and test datasets. However there is still some problem, as CDE lost its performance when there is too much change in class distribution. In this paper, CDE-weight is proposed to reduce the impact of high change in class distribution. the idea is to use many models suitable with many class distributions along with dynamic weighting method that adjusts weight of each model according to its class distribution. Experimented results indicate that CDE-Weight methods are able to reduce the impact of misestimating and improve the classifier performance when train and test data are different.

关键词： Concept drift Classification Cost-sensitive learning Quantification Class distribution estimation Ensemble method

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