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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 3281 条记录，以下是2271-2280 订阅

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Multi-view learning for Classification of X-Ray Crystallography Images 12th

Multi-view Learning for Classification of X-Ray Crystallogra...

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

作者： Lekamge, B. M. Thamali Sowmya, Arcot Newman, Janet Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia CSIRO Mfg 343 Royal Parade Parkville Vic 3052 Australia

ISBN: (纸本)9783319419206;9783319419190

Multi-view learning is a very useful classification technique when multiple, conditionally independent feature sets are available in a dataset. In this paper multi-view learning is used to classify sequences of protein crystallization images that were obtained over a period of time, varying between a few hours to a few months. We introduce the use of the difference image features, along with the original image features, as a second feature set in classifying x-ray crystallography images, after arranging the images according to the timeline of an experiment. Usage of multi-view learning is proposed after carrying out experiments to determine the features that should be used in each view to increase classification accuracy. Random forests are used as the classifier in each view, as preliminary experiments have suggested that it provides higher classification accuracy in crystallography datasets. Accuracy of 97.2% was obtained using multi-view learning based on original and difference features, which is the highest obtained so far in the classification of protein crystallography images.

关键词： X-ray crystallography Protein crystals Multi-view learning Co-training Random forests Difference images

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A Review on Artificial Intelligence Based Parameter Forecasting for Soil-Water Content 12th

A Review on Artificial Intelligence Based Parameter Forecast...

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

作者： Ozcep, Ferhat Yildirim, Eray Tezel, Okan Asci, Metin Karabulut, Savas Ozcep, Tazegul Istanbul Univ Dept Geophys Engn Istanbul Turkey Sakarya Univ Dept Geophys Engn Sakarya Turkey Kocaeli Univ Dept Geophys Engn Kocaeli Turkey Minist Natl Educ Sirinevler Mehmet Sen Okulu Istanbul Turkey

ISBN: (纸本)9783319419206;9783319419190

The purpose of this study, by using an artificial intelligent approaches, is to compare a correlation between geophysical and geotechnical parameters. The input variables for this system are the electrical resistivity reading, the water content laboratory measurements. The output variable is water content of soils. In this study, our data sets are clustered into 120 training sets and 28 testing sets for constructing the fuzzy system and validating the ability of system prediction, respectively. Relationships between soil water content and electrical parameters were obtained by curvilinear models. The ranges of our samples are changed between 1 - 50 ohm. m (for resistivity) and 20 - 60 (%, for water content). An artificial intelligent system (artificial neural networks, Fuzzy logic applications, Mamdani and Sugeno approaches) are based on some comparisons about correlation between electrical resistivity and soil-water content, for Istanbul and Golcuk Soils in Turkey.

关键词： Fuzzy logic applications AI Mamdani and Sugeno approaches Geophysical and geotechnical data

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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3rd international Workshop on Digital Libraries for Musicology, DLfM 2016

ISBN: (纸本)9781450347518

The proceedings contain 11 papers. The topics discussed include: representing and linking music performance data with score information;MORTY: a toolbox for mode recognition and tonic identification;in collaboration with in concert: reflecting a digital library as linked data for performance ephemera;a standard format proposal for hierarchical analyses and representations;approaches to handwritten conductor annotation extraction in musical scores;document analysis for music scores via machine learning;exploring JDISC: some preliminary analyses;digitizing musical scores: challenges and opportunities for libraries;and data generation and multimodal analysis for archival recorded operatic performance.

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Inferring Censored Geo-Information with Non-Representative data 1

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

作者： Zhang, Yu Yang, Tse-Chuan Matthews, Stephen A. SUNY Albany Dept Comp Sci 1400 Washington Ave Albany NY 12222 USA SUNY Albany Dept Sociol 1400 Washington Ave Albany NY 12222 USA Penn State Univ Dept Sociol & Anthropol University Pk PA 16802 USA

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

The goal of this study is to develop a method that is capable of inferring geo-locations for non-representative data. In order to protect privacy of surveyed individuals, most data collectors release coarse geo-information (e.g., tract), rather than detailed geo-information (e.g., street, apt number) when sharing surveyed data. Without the exact locations, many point-based analyses cannot be performed. While several scholars have developed new methods to address this issue, little attention has been paid to how to correct this issue when data are not representative. To fill this knowledge gap, we propose a bias correction method that adjusts for the bias using a bias factor approach. Applying our method to an empirical data set with a known bias associated with gender, we found that our method could generate reliable results despite the non-representativeness of the sample.

关键词： Bias correction Non-representative data Censored geo-location

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LiveDoc: Showing Contextual Information Using Topic Modeling Techniques 12th

LiveDoc: Showing Contextual Information Using Topic Modeling...

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

作者： Deshmukh, Jayati Annervaz, K. M. Sengupta, Shubhashis Pathak, Neetu Accenture Technol Labs Bangalore Karnataka India

ISBN: (纸本)9783319419206;9783319419190

We present a solution named LiveDoc, which augments natural language text documents with relevant contextual background information. This background information helps readers to understand the context of the discourse better by fetching relevant information from other sources such as Wikipedia. Often the readers do not possess all background and supplementary information required for comprehending the purport of a narrative such as a news op-ed article. At the same time, it is not possible for authors to provide all contextual information while addressing a particular topic. LiveDoc processes the information in a document;uses extracted entities to fetch relevant background information in the context of the document from various sources (as defined by user) using semantic matching and topic modeling techniques like Latent Dirichlet Allocation and Hierarchical Dirichlet Process;and presents the background information to the user by augmenting the original document with the fetched information. Reader is then equipped better to understand the document with this additional background information. We present the effectiveness of our solution through extensive experimentation and associated results.

关键词： Information retrieval Topic modeling Natural language processing data contextualization Latent Dirichlet Allocation Hierarchical Dirichlet Process

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Prediction of User App Usage Behavior from Geo-Spatial data 3

Prediction of User App Usage Behavior from Geo-Spatial Data

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3rd ACM international Workshop on Managing and mining Enriched Geo-Spatial data

作者： Zhao, Xiaoxing Qiao, Yuanyuan Si, Zhongwei Yang, Jie Lindgren, Anders Beijing Univ Posts & Telecommun Ctr Data Sci Beijing Peoples R China Lulea Univ Technol SICS Swedish ICT Lulea Sweden

ISBN: (纸本)9781450343091

In the era of mobile Internet, a vast amount of geo-spatial data allows us to gain further insights into human activities, which is critical for Internet Services Providers (ISP) to provide better personalized services. With the pervasiveness of mobile Internet, much evidence show that human mobility has heavy impact on app usage behavior. In this paper, we propose a method based on machine learning to predict users' app usage behavior using several features of human mobility extracted from geo-spatial data in mobile Internet traces. The core idea of our method is selecting a set of mobility attributes (e.g. location, travel pattern, and mobility indicators) that have large impact on app usage behavior and inputting them into a classification model. We evaluate our method using real-world network traffic collected by our self-developed high-speed Traffic Monitoring System (TMS). Our prediction method achieves 90.3% accuracy in our experiment, which verifies the strong correlation between human mobility and app usage behavior. Our experimental results uncover a big potential of geo-spatial data extracted from mobile Internet.

关键词： app usage behavior human mobility geo-spatial data prediction accuracy mobile Internet

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Advances in exploratory pattern analytics on ubiquitous data and social media

Advances in exploratory pattern analytics on ubiquitous data...

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European conference on machine learning, ECML 1989

作者： Atzmüller, Martin Research Center for Information System Design University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany

ISBN: (纸本)9783319417059

Exploratory analysis of ubiquitous data and social media includes resources created by humans as well as those generated by sensor devices. This paper reviews recent advances concerning according approaches and methods, and provides additional review and discussion. Specifically, we focus on exploratory pattern analytics implemented using subgroup discovery and exceptional model mining methods, and put these into context. We summarize recent work on description-oriented community detection, spatio-semantic analysis using local exceptionality detection, and class association rule mining for activity recognition. Furthermore, we discuss results and implications. © Springer international Publishing Switzerland 2016.

关键词： Social networking (online)

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Interestingness Hotspot Discovery in Spatial datasets Using a Graph-Based Approach 12th

Interestingness Hotspot Discovery in Spatial Datasets Using ...

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

作者： Akdag, Fatih Eick, Christoph F. Univ Houston Dept Comp Sci Houston TX 77204 USA

ISBN: (纸本)9783319419206;9783319419190

This paper proposes a novel methodology for discovering interestingness hotspots in spatial datasets using a graph-based algorithm. We define interestingness hotspots as contiguous regions in space which are interesting based on a domain expert's notion of interestingness captured by an interestingness function. In our recent work, we proposed a computational framework which discovers interestingness hotspots in gridded datasets using a 3-step approach which consists of seeding, hotspot growing and post-processing steps. In this work, we extend our framework to discover hotspots in any given spatial dataset. We propose a methodology which firstly creates a neighborhood graph for the given dataset and then identifies seed regions in the graph using the interestingness measure. Next, we grow interestingness hotspots from seed regions by adding neighboring nodes, maximizing the given interestingness function. Finally after all interestingness hotspots are identified, we create a polygon model for each hotspot using an approach that uses Voronoi tessellations and the convex hull of the objects belonging to the hotspot. The proposed methodology is evaluated in a case study for a 2-dimensional earthquake dataset in which we find interestingness hotspots based on variance and correlation interestingness functions.

关键词： Spatial data mining Interestingness hotspot Interestingness function Hotspot discovery Graph-based hotspot growing algorithm Spatial polygon models

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Proceedings of the 10th INDIACom;2016 3rd international conference on Computing for Sustainable Global Development, INDIACom 2016

Proceedings of the 10th INDIACom;2016 3rd International Conf...

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3rd international conference on Computing for Sustainable Global Development, INDIACom 2016

ISBN: (纸本)9789380544199

The proceedings contain 812 papers. The topics discussed include: power consumption analysis across heterogeneous data center using cloudsim;underlying text independent speaker recognition;automatic ration distribution system-a review;evaluation for POS tagger, chunk and resolving issues in word sense disambiguate in machine translation for Hindi to English languages;virtual calling number for ESME;traffic signal preemption (TSP) system by ordinary vehicles in case of emergency based on Internet of Things ecosystem;ICT based communication systems as enabler for technology transfer;adaptability in constant modulus algorithm and optimization for smart antenna systems;design of gain enhanced stacked rectangular dielectric resonator antenna for C-band applications;design of rectangular dielectric resonator antenna using offset micro-strip feed for satellite application;identification of parameters of digital IIR filters using teaching-learning optimization algorithm and statistical inference comparison with particle swarm optimization algorithms;proximal privacy preserving linear classification of horizontally and checkerboard partitioned data;beyond m-score for detection of misusability by malicious insiders;effect of population and bit size on optimization of function by genetic algorithm;and design and advances of cylindrical dielectric resonator antenna - a review.

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Accelerated Variational Inference for Beta-Liouville Mixture learning with Application to 3D shapes recognition 3

Accelerated Variational Inference for Beta-Liouville Mixture...

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3rd international conference on Control, Decision and Information Technologies (CoDIT)

作者： Fan, Wentao Al-Osaimi, Faisal R. Bouguila, Nizar Du, Ji-Xiang Huaqiao Univ Dept Comp Sci & Technol Xiamen Peoples R China Umm Al Qura Univ Dept Comp Engn Coll Comp Syst Mecca Saudi Arabia Concordia Univ CIISE Montreal PQ Canada

ISBN: (纸本)9781509021888

Beta-Liouville mixture models have achieved measurable success in many computer vision and pattern recognition applications. In this paper, we develop a novel algorithm to learn this particular kind of models that have been shown to be very efficient for the clustering of proportional data. Our algorithm is based on an accelerated version of the variational Bayes approach. Experiments show that the developed algorithm work very well for the categorization of 3D shapes.

关键词： Infinite mixture variational Bayes Beta-Liouville 3D shapes

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