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

共 1786 条记录，以下是1341-1350 订阅

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Decoding memory processing from electro-corticography in human posteromedial cortex

Decoding memory processing from electro-corticography in hum...

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4th international Workshop on pattern recognition in Neuroimaging (PRNI)

作者： Schrouff, J. Foster, B. Rangarajan, V. Phillips, C. Mourao-Miranda, J. Parvizi, J. Stanford Univ Lab Behav & Cognit Neurosci Stanford CA 94305 USA Univ Liege Cyclotron Res Ctr B-4000 Liege Belgium UCL Dept Comp Sci London WC1E 6BT England

ISBN: (纸本)9781479941490

Recently, machine learning models have been applied to neuroimaging data, which allow predictions about a variable of interest based on the pattern of activation or anatomy over a set of voxels. these pattern recognition based methods present clear benefits over classical (univariate) techniques, by providing predictions for unseen data, as well as the weights of each feature in the model. machine learning methods have been applied to a range of data, from MRI to EEG. However, these multivariate techniques have scarcely been applied to electrocorticography (ECoG) data to investigate cognitive neuroscience questions. In this work, we used previously published ECoG data from 8 subjects to show that machine learning techniques can complement univariate techniques and be more sensitive to certain effects.

关键词： machine learning electrocorticography episodic memory

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Research of Multiple-type Files Carving Method Based on Entropy

Research of Multiple-type Files Carving Method Based on Entr...

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2015 4th National conference on Electrical,Electronics and Computer Engineering(NCEECE 2015)

作者： Jun GUO Jingsha HE Na HUANG Department of Software Engineering Beijing University of Technology

ISBN: (纸本)9781510819085

File carving is a technique of recovering data from disk without depending on the File System, and the key step is the extraction and reassembly of file fragments. Efficient recognition and extraction of file fragments is not only the prerequisite of recovering file, but also the guarantee of a low false positive rate and high accuracy Digital Forensics. In this paper, when the entropy of file fragments is low, the validation algorithms I used for the extraction are header/footer validation and entropy feature extraction validation, but when the entropy of file fragments is high, besides the previous two algorithms I introduced Bloom filter feature extraction validation, byte frequency distribution(BFD) feature extraction validation and support vector machine(SVM) with supervised learning ability to detect the type of file fragments. After the extraction, I used Parallel Unique Path(PUP) for the reassembly of file fragments. I used DFRWS 2007 carving challenge data set to test my method and the result is better than only using entropy to classify multiple-type files especially in the case of high entropy.

关键词： File Carving File Fragments Entropy Bloom filter BFD SVM PUP

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MEG Decoding Across Subjects

MEG Decoding Across Subjects

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4th international Workshop on pattern recognition in Neuroimaging (PRNI)

作者： Olivetti, Emanuele Kia, Seyed Mostafa Avesani, Paolo Bruno Kessler Fdn NeuroInformat Lab NILab Trento Italy

ISBN: (纸本)9781479941490

Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. the extreme difficulty is related to the structural and functional variability across the subjects. We call this approach decoding across subjects. In this work, we address the problem of decoding across subjects for magnetoencephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one.

关键词： brain decoding transfer learning covariate shift stacked generalization

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Trajectory Clustering for Behavioral pattern learning in Transportation Surveillance 4

Trajectory Clustering for Behavioral Pattern Learning in Tra...

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4th IEEE international conference on Artificial Intelligence with applications in Engineering and Technology (ICAIET)

作者： Choong, Mei Yeen Chin, Renee Ka Yin Yeo, Kiam Beng Teo, Kenneth Tze Kin Univ Malaysia Sabah Fac Engn Modelling Simulat & Comp Lab Mat & Mineral Res Unit Kota Kinabalu Malaysia

ISBN: (纸本)9781479979103

the development of an efficient traffic flow monitoring system has been the main focus for many researchers working in the field. Due to the rapid development in urbanization, the complexity of traffic intersections provides challenges for researchers to detect the underlying traffic scenes. With the emerging video based surveillance system, vehicle trajectory can be extracted for observation and prediction via behavioral pattern learning. Prior to the learning, clustering of the extracted vehicle trajectory data is performed to group the data based on similarity measures. In this paper, the implementation of clustering algorithm on the trajectory data is analyzed and issues concerning the trajectory clustering are discussed.

关键词： traffic surveillance trajectory clustering trajectory learning

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Big data Stream learning with SAMOA 14

Big Data Stream Learning with SAMOA

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14th IEEE international conference on data Mining (IEEE ICDM)

作者： Bifet, Albert De Francisci Morales, Gianmarco HUAWEI Noahs Ark Lab Hong Kong Hong Kong Peoples R China Yahoo Labs Barcelona Spain

ISBN: (纸本)9781479942749

Big data is flowing into every area of our life, professional and personal. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. In this demo, we present SAMOA (Scalable Advanced Massive Online Analysis), an open-source platform for mining big data streams. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm, S4, and Samza. SAMOA is written in Java and is available at http://*** under the Apache Software License version 2.0.

关键词： Big data data mining learning (artificial intelligence) pattern classification pattern clustering regression analysis Apache Software License version 2.0 Java S4 SAMOA Samza Storm big data stream learning big data stream mining classification task clustering task distributed stream processing engines distributed streaming algorithms machine learning tasks open-source platform pluggable architecture programming abstractions regression task scalable advanced massive online analysis Algorithm design and analysis Big data data mining Digital signal processing Engines machine learning algorithms Storms Classification Clustering data Streams Distributed Systems machine learning Regression Toolbox big data storms data mining Samoan Islands Algorithm design and analysis Cluster Analysis machine learning algorithms Engines tool cases Java Java programming language Distributed Systems Digital signal processing pattern recognition Regression (Psychology)

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Smartphone-based activity recognition using hybrid classifier utilizing cloud infrastructure for data analysis

Smartphone-based activity recognition using hybrid classifie...

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4th international conference on Pervasive and Embedded Computing and Communication Systems, PECCS 2014

作者： Yuan, Bingchuan Herbert, John Emamian, Yalda Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9789897580000

learning and recognizing the activities of daily living (ADLs) of an individual is vital when providing an individual with context-aware at-home healthcare. In this work, unobtrusive detection of inhabitants' activities in the home environment is implemented through the smartphone and wearable wireless sensor belt solution. A hybrid classifier is developed by combining threshold-based methods and machine learning mechanisms. Features extracted from the raw inertial sensor data are collected from a Body Area Network (BAN) (consisting of the Zephyr BioHarness sensor and an Android smartphone), and are used to build classification models using different machine learning algorithms. A cloud-based data analytics framework is developed to process different classification models in parallel and to select the most suitable model for each user. the evolving machine learning mechanism makes the model become customizable and self-adaptive by utilizing a cloud infrastructure which also overcomes the limitation of the computing power and storage of a smartphone. Furthermore, we investigate methods for adapting a universal model, which is trained using the data set of all users, to an individual user through an unsupervised learning scheme. the evaluation results of the experiments conducted on eight participants indicate that the proposed approach can robustly identify activities in real-time across multiple individuals: the highest recognition rate achieved 98% after a few runs.

关键词： Smartphones

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Mean shrinkage improves the classification of ERP signals by exploiting additional label information

Mean shrinkage improves the classification of ERP signals by...

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4th international Workshop on pattern recognition in Neuroimaging (PRNI)

作者： Hoehne, Johannes Blankertz, Benjamin Mueller, Klaus-Robert Bartz, Daniel Berlin Inst Technol Neurotechnol Grp Berlin Germany Berlin Inst Technol Dept Machine Learning Berlin Germany Korea Univ Dept Brain & Cognit Engn Seoul South Korea

ISBN: (纸本)9781479941490

Linear discriminant analysis (LDA) is the most commonly used classification method for single trial data in a brain-computer interface (BCI) framework. the popularity of LDA arises from its robustness, simplicity and high accuracy. However, the standard LDA approach is not capable to exploit sublabel information (such as stimulus identity), which is accessible in data from event related potentials (ERPs): it assumes that the evoked potentials are independent of the stimulus identity and dependent only on the users' attentional state. We question this assumption and investigate several methods which extract subclass-specific features from ERP data. Moreover, we propose a novel classification approach which exploits subclass-specific features using mean shrinkage. Based on a reanalysis of two BCI data sets, we show that our novel approach outperforms the standard LDA approach, while being computationally highly efficient.

关键词： Accuracy Brain-computer interfaces Electroencephalography Estimation Feature extraction Robustness Standards Brain-Computer Interfaces Feature extraction Electroencephalography laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Enterprise resource planning Robustness Classification Methods

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Digital Circuit Design using Support Vector machines

Digital Circuit Design using Support Vector Machines

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4th international conference on Communication Systems and Network Technologies (CSNT)

作者： Manikandan, J. Agrawal, V. K. PES Inst Technol Crucible Res & Innovat Bangalore 560085 Karnataka India

ISBN: (纸本)9781479930708

Digital circuits are preferred over its analog counterpart with the invention of microprocessors, microcontrollers, digital signal processors and Field Programmable Gate Arrays (FPGA). Digital circuits in the form of digital arithmetic and digital logics are employed for various applications. On the other side, Support Vector machine (SVM) is considered as a state-of-the-art tool for pattern recognition. In this paper, a novel attempt is made to study, assess and design digital circuits using two-class and multi class support vector machine classifiers. It is reported in literature that computational complexity and the classification time for SVM classifier depends on the number of support vectors required for classification. In order to reduce the number of support vectors, an optimum threshold technique based SVM classifier is employed for the design of digital circuits and it is observed that a maximum of 87.5% of total support vectors are pruned on using the technique.

关键词： Digital circuits pattern Classification machine learning Support Vector machines

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Classification of inter-subject fMRI data based on graph kernels

Classification of inter-subject fMRI data based on graph ker...

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4th international Workshop on pattern recognition in Neuroimaging (PRNI)

作者： Vega-Pons, Sandro Avesani, Paolo Andric, Michael Hasson, Uri Fdn Bruno Kessler NeuroInformat Lab NILab Trento Italy Univ Trento CIMeC Trento Italy

ISBN: (纸本)9781479941490

f the analysis of human brain connectivity networks has become an increasingly prevalent task in neuroimaging. A few recent studies have shown the possibility of decoding brain states based on brain graph classification. Graph kernels have emerged as a powerful tool for graph comparison that allows the direct use of machine learning classifiers on brain graph collections. they allow classifying graphs with different number of nodes and therefore the inter-subject analysis without any kind of previous alignment of individual subject's data. Using whole-brain fMRI data, in this paper we present a method based on graph kernels that provides above-chance accuracy results for the inter-subject discrimination of two different types of auditory stimuli. We focus our research on determining whether this method is sensitive to the relational information in the data. Indeed, we show that the discriminative information is not only coming from topological features of the graphs like node degree distribution, but also from more complex relational patterns in the neighborhood of each node. Moreover, we investigate the suitability of two different graph representation methods, both based on data-driven parcellation techniques. Finally, we study the influence of noisy connections in our graphs and provide a way to alleviate this problem.

关键词： brain decoding connectivity graphs graph kernels brain parcellation inter-subject classification

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Hierarchical Incident Ticket Classification with Minimal Supervision 14

Hierarchical Incident Ticket Classification with Minimal Sup...

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14th IEEE international conference on data Mining (IEEE ICDM)

作者： Maksai, Andrii Bogojeska, Jasmina Wiesmann, Dorothea IBM Res Zurich Saumerstr 4 CH-8803 Ruschlikon Switzerland

ISBN: (纸本)9781479943036

In this paper, we introduce a novel approach for incident ticket classification that aims at minimizing the manual labelling effort while achieving good-quality predictions. To accomplish this, we devise a two-stage technique that employs hierarchical clustering using a combination of graph clustering (community finding) and topic modelling as first stage, followed by either another round of hierarchical clustering or an active learning approach as second stage. We evaluate the performance of our method in terms of manual labelling effort, prediction quality and efficiency on three real-world datasets and demonstrate that classical approaches to text classification are not well suited for incident ticket texts.

关键词： pattern classification pattern clustering text analysis active learning approach community finding good-quality predictions graph clustering hierarchical clustering hierarchical incident ticket text classification manual labelling minimal supervision performance evaluation prediction efficiency prediction quality real-world datasets topic modelling two-stage technique Clustering algorithms Communities Feature extraction Labeling Manuals Monitoring Servers multi-class classification text mining text processing Cluster Analysis Clustering algorithms text mining pattern recognition Servers Feature extraction Performance Evaluation Experiential learning Hierarchical double-stage

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