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1st international Conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Tanuar, Evawaty Heryadi, Yaya Lukas Abbas, Bahtiar Saleh Gaol, Ford Lumban Bina Nusantara Univ Sch Comp Sci Comp Sci Dept Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ Dept Comp Sci BINUS Grad Program Comp Sci Jakarta 11480 Indonesia Univ Katolik Indonesia Atma Jaya Fac Engn CERG Jakarta 12930 Indonesia

ISBN: (纸本)9781538694220

Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be predicted. The data used in this experiment are from the computer science subjects, 6 subjects, 1 laboratories results and the GPA on their graduation year. The techniques used in this experiment are Generalized Linear Model, Deep learning and Decision Tree. From this result, what are the important factors that impact on the result can be extracted to help the students prepared themselves earlier.

关键词： machine learning data mining student's Performance - GPA Soft Computing

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Advances in predictive data mining methods 1st

Advances in predictive data mining methods

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1st international workshop on machine learning and data mining in pattern recognition, MLDM 1999

作者： Hong, Se June Weiss, Sholom M. IBM T.J. Watson Research Center P.O. Box 218 Yorktown HeightsNY10598 United States

ISBN: (纸本)3540665994

Predictive models have been widely used long before the development of the new field that we call data mining. Expanding application demand for data mining of ever increasing data warehouses, and the need for understandability of predictive models with increased accuracy of prediction, all have fueled recent advances in automated predictive methods. We first examine a few successful application areas and technical challenges they present. We discuss some theoretical developments in PAC learning and statistical learning theory leading to the emergence of support vector machines. We then examine some technical advances made in enhancing the performance of the models both in accuracy (boosting, bagging, stacking) and scalability of modeling through distributed model generation. © Springer-Verlag Berlin Heidelberg 1999.

关键词： data mining

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BiblioDAP'21: The 1st workshop on Bibliographic data Analysis and Processing 21

BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysi...

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27th ACM SIGKDD international Conference on Knowledge Discovery and data mining (KDD)

作者： Boukhers, Zeyd Mayr, Philipp Peroni, Silvio Univ Koblenz Landau Inst Web Sci & Technol Koblenz Germany GESIS Leibniz Inst Social Sci Cologne Germany Univ Bologna Dept Class Philol & Italian Studies Res Ctr Open Scholarly Metadata Bologna Italy

ISBN: (纸本)9781450383325

Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side. This processing has several aspects including but not limited to I) Automatic extraction of references from PDF documents, II) Building an accurate citation graph, III) Author name disambiguation, etc. Bibliographic data is heterogeneous by nature and occurs in both structured (e.g. citation graph) and unstructured (e.g. publications) formats. Therefore, it requires data science and machine learning techniques to be processed and analysed. Here we introduce BiblioDAP'21: The 1st workshop on Bibliographic data Analysis and Processing.

关键词： Bibliographic data Digital libraries machine learning data Science

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A fast SVM training algorithm

A fast SVM training algorithm

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1st international workshop on pattern recognition with Support Vector machines

作者： Dong, JX Suen, CY Concordia Univ Ctr Pattern Recognit & Machine Intelligent Montreal PQ H3G 1M8 Canada Concordia Univ Dept Comp Sci Montreal PQ H3G 1M8 Canada

A fast support vector machine (SVM) training algorithm is proposed under SVM's decomposition framework by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Kernel caching plays a key role in reducing the number of kernel evaluations by maximal reusage of cached kernel elements. Extensive experiments have been conducted on a large handwritten digit database MNIst to show that the proposed algorithm is much faster than Keerthi et al.'s improved SMO, about nine times. Combined with principal component analysis, the total training for ten one-against-the-rest classifiers on MNIst took less than an hour. Moreover, the proposed fast algorithm speeds up SVM training without sacrificing the generalization performance. The 0.6% error rate on MNIst test set has been achieved. The promising scalability of the proposed scheme paves a new way to solve more large-scale learning problems in other domains such as data mining.

关键词： support vector machine kernel caching working set selection digest shrinking handwritten digit recognition

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Application of PrefixSpan* Algorithm in Malware Detection Expert System

Application of PrefixSpan* Algorithm in Malware Detection Ex...

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1st international workshop on Education Technology and Computer Science

作者： Wang, Lina Tan, Xiaobin Pan, Jianfeng Xi, Hongsheng Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

ISBN: (纸本)9780769535579

Malware detection is an important application of data mining. Most of the previously developed sequential pattern mining methods are Apriori-like, which still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long. So we need a novel sequential pattern mining method, called PrefixSpan*, which uses brief projection database in stead of projection database in PrefixSpan. In this paper, we propose a behavior-based detection system, which combines data mining and expert system technique to detect malware in our hosts. The PrefixSpan* algorithm mines association rules in the malware behavior sequence database to form malware behavior pattern database;the expert system matches facts and rules and gives the final result. To verify the correctness and effectiveness of our algorithm, we test and analyze some samples in the experiment section.

关键词： malware defection sequential pattern mining PrefixSpan* Algorithm Projected database

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Silicon support vector machine with on-line learning

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international JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2003年第3期17卷 385-404页

作者： Genov, R Chakrabartty, S Cauwenberghs, G Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Training of support vector machines (SVMs) amounts to solving a quadratic programming problem over the training data. We present a simple on-line SVM training algorithm of complexity approximately linear in the number of training vectors, and linear in the number of support vectors. The algorithm implements an on-line variant of sequential minimum optimization (SMO) that avoids the need for adjusting select pairs of training coefficients by adjusting the bias term along with the coefficient of the currently presented training vector. The coefficient assignment is a function of the margin returned by the SVM classifier prior to assignment, subject to inequality constraints. The training scheme lends efficiently to dedicated SVM hardware for real-time pattern recognition, implemented using resources already provided for run-time operation. Performance gains are illustrated using the Kerneltron, a massively parallel mixed-signal VLSI processor for kernel-based real-time video recognition.

关键词： support vector machine (SVM) large margin classifiers quadratic programming (QP) sequential minimum optimization (SMO) matrix-vector multiplication (MVM) pattern recognition kernel machines analog array processors

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Histogram-Based Fisher Information Embedding for Manifolds Clustering and Visualization

Histogram-Based Fisher Information Embedding for Manifolds C...

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Zou, Jian Liu, ChuanCai Zhang, Yue Nanjing Univ Sci & Technol Dept Comp Sci & Technol Nanjing 210094 Peoples R China

ISBN: (纸本)9781424441990

In this paper, a nonparametric histogram-based fisher information embedding method is presented for clustering and visualizing data sets with non-Euclidean geometric structures. It is on the assumption that each data set is derived from a probability distribution that can be characterized by a probability density function (PDF) lying on a statistical manifold. The dissimilarities between data sets can be quantified with the corresponding geodesic distances on statistical manifold from the views of information geometry. Our method is designed to convert data distribution information into simplex discretely, thereby a similarity measure can be adopted with simplex geometry, further clustering or visualizing original manifolds can be performed on a low-dimensional Euclidean subspace via manifold learning. Experiments on clustering Swiss Roll and s-curve submanifolds and reconstructing a sphere demonstrate the effectiveness of our method.

关键词： Information geometry statistical manifold information embedding manifold learning clustering visualization

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Decision Support System (DSS) - Form, Development and Future

Decision Support System (DSS) - Form, Development and Future

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1st international workshop on Education Technology and Computer Science

作者： Fang, Bin Tianjin Univ Sch Management Tianjin 300072 Peoples R China

The Decision Support System (DSS) applies various data and models to Human-machine Interface (HMI) to assist decision-makers at each level in achieving scientific decisions. The DSS was originated in 1970s, but has se... 详细信息

ISBN: (纸本)9780769535579

关键词： DSS data Warehouse OLAP data mining

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Symbolic learning techniques in paper document processing 1st

Symbolic learning techniques in paper document processing

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1st international workshop on machine learning and data mining in pattern recognition, MLDM 1999

作者： Altamura, Oronzo Esposito, Floriana Lisi, Francesca A. Malerba, Donato Dipartimento di Informatica Università degli Studi di Bari via Orabona 4 Bari70126 Italy

ISBN: (纸本)3540665994

WISDOM++ is an intelligent document processing system that transforms a paper document into HTML/XML format. The main design requirement is adaptivity, which is realized through the application of machine learning methods. This paper illustrates the application of symbolic learning algorithms to the first three steps of document processing, namely document analysis, document classification and document understanding. machine learning issues related to the application are: Efficient incremental induction of decision trees from numeric data, handling of both numeric and symbolic data in first-order rule learning, learning mutually dependent concepts. Experimental results obtained on a set of real-world documents are illustrated and commented. © Springer-Verlag Berlin Heidelberg 1999.

关键词： data handling

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Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data

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pattern recognition LETTERS 2015年第Nov.15期66卷 22-30页

作者： Ringeval, Fabien Eyben, Florian Kroupi, Eleni Yuce, Anil Thiran, Jean-Philippe Ebrahimi, Touradj Lalanne, Denis Schueller, Bjoern Tech Univ Munich Machine Intelligence & Signal Proc Grp MMK D-80333 Munich Germany Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Signal Proc Lab LTS5 CH-1015 Lausanne Switzerland Univ Fribourg Document Image & Voice Anal CH-1700 Fribourg Switzerland Univ London Imperial Coll Sci Technol & Med Dept Comp Machine Learning Grp London SW7 2AZ England Univ Passau Chair Complex Syst Engn D-94032 Passau Germany

Automatic emotion recognition systems based on supervised machine learning require reliable annotation of affective behaviours to build useful models. Whereas the dimensional approach is getting more and more popular for rating: affective behaviours in continuous time domains, e.g., arousal and valence, methodologies to take into account reaction lags of the human raters are still rare. We therefore investigate the relevance of using machine learning algorithms able to integrate contextual information in the modelling, like long short-term memory recurrent neural networks do, to automatically predict emotion from several (asynchronous) raters in continuous time domains, i.e., arousal and valence. Evaluations are performed on the recently proposed RECOLA multimodal database (27 subjects, 5 min of data and six raters for each), which includes audio, video, and physiological (ECG, EDA) data. In fact, studies uniting audiovisual and physiological information are still very rare. Features are extracted with various window sizes for each modality and performance for the automatic emotion prediction is compared for both different architectures of neural networks and fusion approaches (feature-level/decision-level). The results show that: (i) LstM network can deal with (asynchronous) dependencies found between continuous ratings of emotion with video data, (ii) the prediction of the emotional valence requires longer analysis window than for arousal and (iii) a decision-level fusion leads to better performance than a feature-level fusion. The best performance (concordance correlation coefficient) for the multimodal emotion prediction is 0.804 for arousal and 0.528 for valence. (C) 2014 Elsevier B.V. All rights reseived.

关键词： Context-learning long short-term memory recurrent neural networks Audiovisual and physiological data Continuous affect analysis Multi-task learning Multitime resolution features extraction Multimodal fusion

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