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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009"

共 98 条记录，以下是1-10 订阅

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machine learning and data mining in pattern recognition - 6th international conference, mldm 2009, Proceedings

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6th international conference on machine learning and data mining in pattern recognition, mldm 2009

ISBN: (纸本)3642030696

the proceedings contain 61 papers. the topics discussed include improved comprehensibility and reliability of explanations via restricted halfspace discretization;selection of subsets of ordered features in machine learning;combination of vector quantization and visualization;discretization of target attributes for subgroup discovery;preserving privacy in time series data classification by discretization;sequential EM for unsupervised adaptive Gaussian mixture model based classifier;optimal double-kernel combination for classification;a linear classification method in a very high dimensional space using distributed representation;PMCRI: a parallel modular classification rule induction framework;dynamic score combination: a supervised and unsupervised score combination method;and ODDboost.

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Fast pattern recognition and Deep learning Using Multi-Rooted Binary Decision Diagrams

Fast Pattern Recognition and Deep Learning Using Multi-Roote...

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

作者： Bugaychenko, Dmitry Zubarevich, Dmitry St Petersburg State Univ St Petersburg 199034 Russia

ISBN: (纸本)9783319089799;9783319089782

Binary decision diagrams (BDD) is a compact and efficient representation of Boolean functions with extensions available for sets and finite-valued functions. the key feature of the BDD is an ability to employ internal structure (not necessary known upfront) of an object being modelled in order to provide a compact in-memory representation. In this paper we propose application of the BDD for machine learning as a tool for fast general pattern recognition. Multiple BDDs are used to capture a sets of training samples (patterns) and to estimate the similarity of a given test sample with the memorized training sets. then, having multiple similarity estimates further analysis is done using additional layer of BDDs or common machine learning techniques. We describe training algorithms for BDDs (supervised, unsupervised and combined), an approach for constructing multi-layered networks combining BDDs with traditional artificial neurons and present experimental results for handwritten digits recognition on the MNIST dataset.

关键词： pattern recognition binary decision diagrams

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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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A Time Series Model of the Writing Process 1

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

作者： Volkovich, Zeev ORT Braude Coll Engn Software Engn Dept IL-21982 Karmiel Israel

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

the necessity to operate with the huge number of anonymous documents abounding on the Internet is initiating the study of new methods for authorship recognition. the principal weakness of the methods used in this area is that they assess the similarity of text styles without any regard to their surroundings. this paper proposes a novel mathematical model of the writing process striving to quantify this dependency. A text is divided into a series of sequential sub-documents, which are represented via term histograms. the histograms proximity is estimated through a simple probability distance. Intending to typify the text writing style, a new characteristic representing the mean distance between a current sub-document and numerous earlier ones is advanced. An empirical distribution over the whole document of this feature specifies the writing style. So, dissimilarity of such distributions indicates a difference in the writing styles, and their coincidence implies the styles' identity. Numerical experiments demonstrate high potential ability of the proposed approach.

关键词： learning of internal representations and models mining text documents Text mining Time series and sequential pattern mining Authorship recognition

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Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths 1

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

作者： Vinh-Trung Luu Ripken, Mathis Forestier, Germain Fondement, Frederic Muller, Pierre-Alain Univ Haute Alsace MIPS 12 Rue Freres Lumiere F-68093 Mulhouse France

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

this work takes place in the context of conversion rate optimization by enhancing the user experience during navigation on e-commerce web sites. the requirement is to be able to segment visitors into meaningful clusters, which can then be targeted with specific call-to-actions, in order to increase the web site turnover. this paper presents an original approach, which equally combines global-and local-alignment techniques (Needleman-Wunsch and Smith-Waterman) in order to automatically segment visitors according to the sequence of visited pages. Experimental results on synthetic datasets show that our approach out-performs other typically used alignment metrics, such as hybrid approaches or Dynamic Time Warping.

关键词： Web mining Sequential pattern mining Clustering

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learning betting tips from users' bet selections

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6th international conference on machine learning and data mining in pattern recognition, mldm 2009

作者： Štrumbelj, Erik Šikonja, Marko Robnik Kononenko, Igor Faculty of Computer and Information Science University of Ljubljana Trzaška 25 Ljubljana 1000 Slovenia

ISBN: (纸本)3642030696

In this paper we address the problem of using bet selections of a large number of mostly non-expert users to improve sports betting tips. A similarity based approach is used to describe individual users' strategies and we propose two different scoring functions to evaluate them. the information contained in users' bet selections improves on using only bookmaker odds. Even when only bookmaker odds are used, the approach gives results comparable to those of a regression-based forecasting model. © 2009 Springer Berlin Heidelberg.

关键词： data mining

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Optimal Double-Kernel Combination for Classification

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6th international conference on machine learning and data mining in pattern recognition

作者： Wang, Feng Zhang, Hongbin Beijing Univ Sci & Technol Coll Comp Sci Beijing 100124 Peoples R China

ISBN: (纸本)9783642030697

Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.

关键词： Classification kernel learning pattern recognition

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Building FP-Tree on the Fly: Single-Pass Frequent Itemset mining 12th

Building FP-Tree on the Fly: Single-Pass Frequent Itemset Mi...

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

作者： Shahbazi, Nima Soltani, Rohollah Gryz, Jarek An, Aijun York Univ Dept Comp Sci & Engn Toronto ON Canada

ISBN: (纸本)9783319419206;9783319419190

the FP-Growth algorithm has been studied extensively in the field of frequent pattern mining. the algorithm offers the advantage of avoiding costly database scans in comparison with Apriori-based algorithms. However, since it still requires two database scans, it cannot be used on streaming data. Also, the algorithm is designed for static datasets, where the input transactions are fixed and thus cannot be used for incremental or interactive mining. Existing incremental mining algorithms are not easily adoptable for on-the-fly, fast, and memory efficient FP-tree mining. In this paper we propose a novel SPFP-tree (single pass frequent pattern tree) algorithm that scans the database only once and provides the same tree as FP-Growth. Our algorithm changes the tree structure dynamically to create a highly compact frequency-ordered tree on the fly. With the insertion of each new transaction our algorithm dynamically maintains a tree identical to an FP-tree. Experimental results show the efficiency of the SPFP-tree algorithm in both incremental and interactive mining of frequent patterns.

关键词： data mining Frequent pattern Incremental mining Association rule Interactive mining

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An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding

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6th international conference on machine learning and data mining in pattern recognition

作者： Nikolopoulos, Spiros Papadopoulos, Georgios th. Kompatsiaris, Ioannis Patras, Ioannis CERTH Informat & Telemat Inst 6th Km Charilaou Thermi Rd Thessaloniki Greece Univ London Queen Mary Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9783642030697

this work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and pattern recognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic, inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis, tasks showed improvement performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.

关键词： Image analysis

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A Clustering Approach for Discovering Intrinsic Clusters in Multivariate Geostatistical data 12th

A Clustering Approach for Discovering Intrinsic Clusters in ...

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

作者： Fouedjio, Francky CSIRO Mineral Resources Perth WA Australia

ISBN: (纸本)9783319419206;9783319419190

Multivariate georeferenced data have become omnipresent in the many scientific fields and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in terms of a concept of dissimilarity. In this work, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the multivariate spatial dependence structure of data. It integrates existing methods to find the optimal cluster number. the capability of the proposed approach to provide spatially compact, connected and meaningful clusters is illustrated to the National Geochemical Survey of Australia data.

关键词： Clustering Geostatistics Multivariate data Non-parametric

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