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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1291-1300 订阅

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Large-Scale Stylistic Analysis of Formality in Academia and Social Media 17th

Large-Scale Stylistic Analysis of Formality in Academia and ...

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17th international conference on Web Information Systems Engineering (WISE)

作者： thin Nguyen Venkatesh, Svetha Dinh Phung Deakin Univ Burwood Australia

ISBN: (纸本)9783319487434;9783319487427

the dictum `publish or perish' has influenced the way scientists present research results as to get published, including exaggeration and overstatement of research findings. this behavior emerges patterns of using language in academia. For example, recently it has been found that the proportion of positive words has risen in the content of scientific articles over the last 40 years, which probably shows the tendency in scientists to exaggerate and overstate their research results. the practice may deviate from impersonal and formal style of academic writing. In this study the degree of formality in scientific articles is investigated through a corpus of 14 million PubMed abstracts. three aspects of stylistic features are explored: expressing emotional information, using first person pronouns to refer to the authors, and mixing English varieties. Trends of these stylistic features in scientific publications for the last four decades were discovered. A comparison on the emotional information with other online user-generated media, including online encyclopedias, web-logs, forums, and micro-blogs, was conducted. Advances in cluster computing are employed to process large scale data, with 5.8 terabytes and 3.6 billions of data points from all the media. the results suggest the potential of pattern recognition in data at scale.

关键词： Big data Apache Spark Stylistic features Academia Social media

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Towards the detection of learner's uncertainty through face

Towards the detection of learner's uncertainty through face

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international conference on User Science and Engineering (i-USEr)

作者： Siti Asmaq Ahmad Daud Syaheerah Lebai Lutfi School of Computer Sciences Universiti Sains Malaysia Penang Malaysia

ISBN: (纸本)9781509026326

this research aims to detect uncertainty based on facial expression in a learning context with the use of the Facial Action Coding System (FACS). Although FACS has been used to categorize facial uncertainty, very few studies have worked in this field. Most studies rather focus on uncertainty detection using voice, even though uncertainty is more apparent through facial cues compared to vocal cues, and this warrants for the collection and analysis of a facial corpus. Hence, an effort to collect a facial corpus of uncertainty were made and the corpus is then analyzed. data was collected through an experiment that entailed using stimuli to induce the uncertainty of the subject. the data was annotated in order to verify the images before proceeding to preprocessing and feature extraction techniques. the feature extraction of the images was carried out using Gabor Wavelets and classification to train the data is used Support Vector machine (SVM).

关键词： Uncertainty Feature extraction Face recognition databases Support vector machines Face data collection

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machine learning and Optimization Tools for Big Multimedia data Analytics

Machine Learning and Optimization Tools for Big Multimedia D...

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2016 4th IEEE international conference on Cloud Computing and Intelligence Systems（IEEE CCIS2016）

作者： Moncef Gabbouj Tampere University of Technology

Big data analytics is the core engine in modern decision-making environments. In this talk, we discuss modern signal processing, machine learning, pattern recognition and optimization tools recently developed and used in multimedia data bases with a special emphasis on Big multimedia data. Classification and search in large media repositories will be the main targeted *** talk deals with a new paradigm for multimedia search based on *** present an alternative approach to classical search engines for information retrieval,which can be used for Big and generic multimedia *** introduce an incremental evolution scheme within a collective network of（evolutionary）binary classifier（CNBC）*** proposed framework addresses the problems of feature/class scalability and achieves high classification and content-based retrieval performances over dynamic image *** secret behind the success of CNBC is a novel design to implement the backbone of CNBC,namely the binary *** is a special neural network,which is optimally designed using the recently developed evolutionary optimization algorithm called multi-dimensional particle swarm *** swarm optimization（PSO）is population based stochastic search and optimization process,which was introduced in 1995 by Kennedy and *** goal is to converge to the global optimum of some multi-dimensional fitness *** novel techniques,which extend the basic PSO algorithm,are *** a multidimensional search space where the optimum dimension is unknown,swarm particles can seek both positional and dimensional *** resulting MD-PSO plays a key role in developing machine learning tools for data classification and feature *** content-based multimedia search engines available today rely heavily on low-level ***,such features extracted automatically usually lack discrimination power needed for accurate description of the image c

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A Privacy-preserving Crowd Movement Analysis by k-member Clustering of Face Images 4

A Privacy-preserving Crowd Movement Analysis by <i>k</i>-mem...

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4th international conference on Informatics, Electronics & Vision (lCIEV)

作者： Honda, Katsuhiro Omori, Masahiro Ubukata, Seiki Notsu, Akira Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan

ISBN: (纸本)9781467369022

Crowd movement analysis is an important issue in social design. this paper studies an machine learning approach to crowd movement estimation through face image recognition. Although high performance face recognition is a powerful tool in individual authentication with surveillance camera images in public spaces, utilization of personal information is often hesitated under fear of privacy violation. In this paper, a privacy preserving framework for crowd movement analysis is proposed considering k-anonymization of face image features. k-anonymity is a quantitative measure of secureness in data mining and is expected to enhance the utility of personal information. An experimental result demonstrates the applicability of the secure framework in capturing crowd movement characteristics even if individual features are k-aonymized so that each individual is not distinguishable from others k - 1 ones.

关键词： cameras data mining data privacy face recognition learning (artificial intelligence) parallel processing high performance face image recognition k-anonymization k-member clustering machine learning approach privacy violation privacy-preserving crowd movement analysis surveillance camera image Cameras data privacy Face Face recognition Image recognition Logic gates pattern matching data privacy Facial recognition Cameras data mining Image recognition Face Face pattern matching Logic gates PARALLEL PROCESSING (COMPUTERS) Movements

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Hand dorsal vein recognition based on deep learning 4th

Hand dorsal vein recognition based on deep learning

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4th international conference on Electronics, Communications and Networks, CECNet2014

作者： Wang, Yiding Cao, Haijun Gong, He Li, Kefeng College of Electronic Information Engineering North China University of Technology Beijing China College of Information Science and Electrical Engineering Shandong Jiaotong University Shandong China

ISBN: (纸本)9781138028302

A novel method based on deep learning is proposed for hand dorsal vein recognition. In the paper, described firstly is the acquisition of the hand dorsal vein images and preprocessing of the images, which is followed by extracting Center-Symmetric Local Binary pattern (CSLBP) features. then deep learning used to train features for classification will be discussed, particularly, and restricted Boltzmann machine is focused. Finally, combination encode of different scales is introduced to increase the recognition rate. Correlation classifier is taken as the final classifier. From the experiments, it can be seen that the proposed method achieves a high recognition rate of 97.4%. © 2015 Taylor & Francis Group, London.

关键词： Deep learning

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An exact graph edit distance algorithm for solving pattern recognition problems 4

An exact graph edit distance algorithm for solving pattern r...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Abu-Aisheh, Zeina Raveaux, Romain Ramel, Jean-Yves Martineau, Patrick Université François Rabelais Tours37200 France

ISBN: (纸本)9789897580765

Graph edit distance is an error tolerant matching technique emerged as a powerful and flexible graph matching paradigm that can be used to address different tasks in pattern recognition, machine learning and data mining;it represents the minimum-cost sequence of basic edit operations to transform one graph into another by means of insertion, deletion and substitution of vertices and/or edges. A widely used method for exact graph edit distance computation is based on the A∗ algorithm. To overcome its high memory load while traversing the search tree for storing pending solutions to be explored, we propose a depth-first graph edit distance algorithm which requires less memory and searching time. An evaluation of all possible solutions is performed without explicitly enumerating them all. Candidates are discarded using an upper and lower bounds strategy. A solid experimental study is proposed;experiments on a publicly available database empirically demonstrated that our approach is better than the A∗ graph edit distance computation in terms of speed, accuracy and classification rate.

关键词： Classification (of information)

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ICPRAM 2015 - 4th international conference on pattern recognition Applications and Methods, Proceedings

ICPRAM 2015 - 4th International Conference on Pattern Recogn...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

ISBN: (纸本)9789897580772

the proceedings contain 94 papers. the topics discussed include: naive Bayes classifier with mixtures of polynomials;representation optimization with feature selection and manifold learning in a holistic classification framework;discriminative kernel feature extraction and learning for object recognition and detection;normalised diffusion cosine similarity and its use for image segmentation;on selecting useful unlabeled data using multi-view learning techniques;user-driven nearest neighbour exploration of image archives;HyperSAX: fast approximate search of multidimensional data;a hybrid BLSTM-HMM for spotting regular expressions;mobility assessment of demented people using pose estimation and movement detection - an experimental study in the field of ambient assisted living;improvement of recovering shape from endoscope images using RBF neural network;dismantling composite visualizations in the scientific literature;and automatic tooth identification in dental panoramic images with atlas-based models.

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ICPRAM 2015 - 4th international conference on pattern recognition Applications and Methods, Proceedings

ICPRAM 2015 - 4th International Conference on Pattern Recogn...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

ISBN: (纸本)9789897580765

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learning dynamic systems from time-series data an application to gene regulatory networks 4

Learning dynamic systems from time-series data an applicatio...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Timoteo, Ivo J.P.M. Holden, Sean B. University of Cambridge Computer Laboratory William Gates Building 15 JJ Thomson Avenue CambridgeCB3 OFD United Kingdom

ISBN: (纸本)9789897580772

We propose a local search approach for learning dynamic systems from time-series data, using networks of differential equations as the underlying model. We evaluate the performance of our approach for two scenarios: first, by comparing with an l1-regularization approach under the assumption of a uniformly weighted network for identifying systems of masses and springs;and then on the task of learning gene regulatory networks, where we compare it with the best performers in the DREAM4 challenge using the original dataset for that challenge. Our method consistently improves on the performance of the other methods considered in both scenarios.

关键词： Differential equations

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Automatic Image annotation using convex deep learning models 4

Automatic Image annotation using convex deep learning models

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Sarangi, Niharjyoti Sekhar, C. Chandra Department of CSE Indian Institute of Technology Madras Chennai India

ISBN: (纸本)9789897580772

Automatically assigning semantically relevant tags to an image is an important task in machine learning. Many algorithms have been proposed to annotate images based on features such as color, texture, and shape. Success of these algorithms is dependent on carefully handcrafted features. Deep learning models are widely used to learn abstract, high level representations from raw data. Deep belief networks are the most commonly used deep learning models formed by pre-training the individual Restricted Boltzmann machines in a layer-wise fashion and then stacking together and training them using error back-propagation. In the deep convolutional networks, convolution operation is used to extract features from different sub-regions of the images to learn better representations. To reduce the time taken for training, models that use convex optimization and kernel trick have been proposed. In this paper we explore two such models, Tensor Deep Stacking Network and Kernel Deep Convex Network, for the task of automatic image annotation. We use a deep convolutional network to extract high level features from raw images, and then use them as inputs to the convex deep learning models. Performance of the proposed approach is evaluated on benchmark image datasets.

关键词： Tensors

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