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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1241-1250 订阅

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Performance of Deep learning Algorithms vs. Shallow Models, in Extreme Conditions - Some Empirical Studies 7th

Performance of Deep Learning Algorithms vs. Shallow Models, ...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Banerjee, Samik Bhattacharjee, Prateep Das, Sukhendu Indian Inst Technol Madras Madras 600036 Tamil Nadu India

ISBN: (纸本)9783319699004;9783319698991

Deep convolutional neural networks (DCNN) successfully exhibit exceptionally good classification performance, despite their massive size. the effect of a large value of noise term, as irreducible error in Expected Prediction Error (EPE) is first discussed. through extensive systematic experiments, we show how in extreme conditions the traditional approaches fare at par with large neural networks, which generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks trained for classification barely fit a random labeling of the training data as an extreme condition to learn. this phenomenon is quantitatively unaffected even if we train the CNNs with completely inseparable data. this can be due to large degree of corruption of the entire data by random noise or random labels associated with data due to observation error. We corroborate these experimental findings by showing that depth six CNN (VGG-6) fails to overcome large noise in image signals.

关键词： Convolutional neural networks Noise Classification SVM EPE

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Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG data 7th

Feature Selection and Fuzzy Rule Mining for Epileptic Patien...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Dasgupta, Abhijit Nayak, Losiana Das, Ritankar Basu, Debasis Chandra, Preetam De, Rajat K. Indian Stat Inst Machine Intelligence Unit Kolkata India West Bengal Univ Technol Dept Bioinformat Kolkata India Med Coll & Hosp Dept Neuromed Kolkata India

ISBN: (纸本)9783319699004;9783319698991

In this paper, we create EEG data derived signatures for differentiating epileptic patients from normal individuals. Epilepsy is a neurological condition of human beings, mostly treated based on a patient's seizure symptoms. Clinicians face immense difficulty in detecting epileptic patients. Here we define brain region-connection based signatures from EEG data with help of various machine learning techniques. these signatures will help the clinicians in detecting epileptic patients in general. Moreover, we define separate signatures by taking into account a few demographic features like gender and age. Such signatures may aid the clinicians along with the generalized epileptic signature in case of complex decisions.

关键词： Epileptic signature Brain connectivity network Fuzzy rule mining EEGLAB MATLAB

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pattern recognition in residential end uses of water using artificial neural networks and other machine-learning techniques 15

Pattern recognition in residential end uses of water using a...

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15th international conference on Computing and Control for the Water Industry, CCWI 2017

作者： Carranza, Juan Carlos Ibáñez Morales, Roberto Díaz Sánchez, Jose Antonio Canal de Isabel II Santa Engracia 125 Madrid28003 Spain Treelogic. Parque Tecnológico de Asturias parcela 30 Llanera Asturias33428 Spain

machine-learning and other Artificial Intelligence techniques havenowadaysmanypractical applications in engineering, science or everyday life. In the water industry, there is also a broad scope of potential applications. In this paper,it will be presented a system developed by Canal de Isabel IIto identify residential use of water in its different appliances, based on records from precision water meters equipped with pulse emitters. Developed models are based on Support Vector machines, and Artificial Neural Network paradigms. Training data sets for the models have been extracted from a sample of about 300 residential users in the Region of Madrid (Spain), monitored since 2008. In this time, more than 35 million of water use eventshave been registered and about 15 million hours of water consumption monitored. machine-learning techniques have proved to be an accurate andsuitable method for automate this task that otherwise should require a huge number of man-hours of processing by operators. © 2020 CCWI 2017 - 15th international conference on Computing and Control for the Water Industry. All rights reserved.

关键词： Support vector machines

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Extraction of Phenotypic Traits for Drought Stress Study Using Hyperspectral Images 7th

Extraction of Phenotypic Traits for Drought Stress Study Usi...

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7th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Bhugra, Swati Agarwal, Nitish Yadav, Shubham Banerjee, Soham Chaudhury, Santanu Lall, Brejesh Indian Inst Technol Dept Elect Engn Delhi India

ISBN: (纸本)9783319699004;9783319698991

High-throughput identification of digital traits encapsulating the changes in plant's internal structure under drought stress, based on hyperspectral imaging (HSI) is a challenging task. this is due to the high spectral and spatial resolution of HSI data and lack of labelled data. therefore, this work proposes a novel framework for phenotypic discovery based on autoencoders, which is trained using Simple Linear Iterative Clustering (SLIC) superpixels. the distinctive archetypes from the learnt digital traits are selected using simplex volume maximisation (SiVM). their accumulation maps are employed to reveal differential drought responses of wheat cultivars based on t-distributed stochastic neighbour embedding (t-SNE) and the separability is quantified using cluster silhouette index. Unlike prior methods using raw pixels or feature vectors computed by fusing predefined indices as phenotypic traits, our proposed framework shows potential by separating the plant responses into three classes with a finer granularity. this capability shows the potential of our framework for the discovery of data-driven phenotypes to quantify drought stress responses.

关键词： Autoencoders SLIC superpixels Hyperspectral Drought stress t-SNE Deep learning SiVM

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pattern classification of hand movements using time domain features of electromyography 17

Pattern classification of hand movements using time domain f...

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4th international conference on Movement Computing, MOCO 2017

作者： Robinson, Carl Peter Li, Baihua Meng, Qinggang Pain, Matthew T.G. Department of Computer Science Loughborough University LoughboroughLE113TU United Kingdom School of Sport Exercise and Health Sciences Loughborough University LoughboroughLE113TU United Kingdom

ISBN: (纸本)1595930361

Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect the electrical signals of muscle activity and perform subsequent mechanical actions. Despite several decades' research, robust, responsive and intuitive control schemes remain elusive. Current commercial hardware advances offer a variety of movements but the control systems are unnatural, using sequential switching methods triggered by specific sEMG signals. However, recent research with pattern recognition and simultaneous and proportional control shows good promise for natural myoelectric control. this paper investigates several sEMG time domain features using a series of hand movements performed by 11 subjects, taken from a benchmark database, to determine if optimal classification accuracy is dependent on feature set size. the features were extracted from the data using a sliding window process and applied to five machine learning classifiers, of which Random Forest consistently performed best. Results suggest a few simple features such as Root Mean Square and Waveform Length achieve comparable performance to using the entire feature set, when identifying the hand movements, although further work is required for feature optimisation. © 2017 ACM.

关键词： Electromyography

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Sparse pattern Representation in a Realistic Recurrent Spiking Neural Network 26th

Sparse Pattern Representation in a Realistic Recurrent Spiki...

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26th international conference on Artificial Neural Networks (ICANN)

作者： Garrido, Jesus A. Ros, Eduardo Univ Granada CITIC UGR Dept Comp Architecture & Technol Granada Spain

ISBN: (纸本)9783319686004;9783319685991

the reliability of brain learning strongly depends on the quality of spatio-temporal pattern representation. However, the way neuronal mechanisms contribute to generate distinguishable representations remains largely unknown. In this sense, sparse codes combine the representational capacity of dense codes and the accuracy of learning with local codes. the most representative structure of sparse coding in the nervous system is the cerebellum since it accounts in its input layer with around 50 billion granule cells (which represent half of the neurons in the brain). Each granule cell receives input, on average, from only 4 mossy fibers [2]. In this work we have reproduced a 100um-length cube of the cerebellar input layer according to experimental data of neuronal density, electrical properties of neurons, connectivity constraints and dendritic length. the network was equipped with leaky integrate and fire neurons with adaptive threshold, since this neuron model has shown very effective in pattern transmission [1]. the input layer has been stimulated with four overlapping current patterns and oscillations in theta-frequency band (8 Hz) as previously presented in [3]. By connecting the resulting activity of the granule cells to a single-layer perceptron we have assessed the quality of pattern representation. the perceptron obtained 97 percent of pattern recognition accuracy in the test subset. Both recurrent inhibition and adaptive threshold contributed to keep granule cell activity sparse, while theta-band frequency oscillations at the input enabled synchronization between successive neuronal layers. © Springer international Publishing AG 2017.

关键词： pattern representation Recurrent networks Oscillations Cerebellum

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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8th Balkan conference in Informatics, BCI 2017

ISBN: (纸本)9781450352857

the proceedings contain 29 papers. the topics discussed include: machine learning-based approach to automatic POS tagging of Macedonian language;a novel model for measuring component-based system agility;information retrieval with reinforced word classes;towards dynamic cross-platform component-driven applications with girders elements framework;a time-critical mobile application based on ECG medical monitoring;reasons for and benefits of teaching internet of things basics in the eve of the 4th industrial revolution;a systematic mapping study of computer vision approaches based on deep learning and neural network;extended Tuple constraint type in relational and xml data model - definition and enforcement;structured component and connector communication;Albanian dynamic dactyls recognition using Kinect technology and DTW;using lexical resources for irony and sarcasm classification;extracting text keywords using WordNet;the invalidity of validating emotional multi-agent systems simulations;the new era of AI will revolutionize our wellness;proposed architecture to manage critical states predictions in IoT applications;a survey on access control mechanisms in e-commerce environments;and stimulating intellectual activity with adaptive environment (SMILE).

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Global flow and temporal-shape descriptors for human action recognition from 3D reconstruction data 1

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13th international conference on machine learning and data Mining in pattern recognition, MLDM 2017

作者： Papadopoulos, Georgios th Daras, Petros Information Technologies Institute Centre for Research and Technology Hellas Thermi Thessaloniki57001 Greece

ISBN: (数字)9783319624167

ISBN: (纸本)9783319624150

In this paper, global-level view-invariant descriptors for human action recognition using 3D reconstruction data are proposed. 3D reconstruction techniques are employed for addressing two of the most challenging issues related to human action recognition in the general case, namely view-variance and the presence of (self-) occlusions. Initially, a set of calibrated Kinect sensors are employed for producing a 3D reconstruction of the performing subjects. Subsequently, a 3D flow field is estimated for every captured frame. For performing action recognition, a novel global 3D flow descriptor is introduced, which achieves to efficiently encode the global motion characteristics in a compact way, while also incorporating spatial distribution related information. Additionally, a new global temporal-shape descriptor that extends the notion of 3D shape descriptions for action recognition, by including temporal information, is also proposed. the latter descriptor efficiently addresses the inherent problems of temporal alignment and compact representation, while also being robust in the presence of noise. Experimental results using public datasets demonstrate the efficiency of the proposed approach. © Springer international Publishing AG 2017.

关键词： Image reconstruction

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Personalized visualization based upon wavelet transform for interactive software customization 13th

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13th international conference on machine learning and data Mining in pattern recognition, MLDM 2017

作者： Yuan, Xiaobu Kaler, Manpreet Mulpuri, Vijaya University of Windsor WindsorONN9B3P4 Canada

ISBN: (纸本)9783319624150

this paper presents an enhancement to a prior work on interactive software customization. By conserving the historical data of belief states collected during a dialogue and analyzing the dataset with wavelet transform, a new approach is developed to categorize users into different clusters so that personalized visualization can be introduced to assist software clients in the process of requirement elicitation. the results of a usability study demonstrate improved efficiency of performance for both experienced and non-experienced users when software visualization is personalized according to users’ level of product knowledge and/or skills of software development. © Springer international Publishing AG 2017.

关键词： Wavelet transforms

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Analyzing Load Profiles of Electricity Consumption by a Time Series data Mining Framework 1

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4th international conference on HCI in Business, Government and Organizations (HCIBGO) held as part of the 19th international conference on Human-Computer Interaction (HCI)

作者： Wu, I-Chin Chen, Tzu-Li Chen, Yen-Ming Liu, Tzu-Chi Chen, Yi-An Natl Taiwan Normal Univ Grad Inst Lib & Informat Studies Taipei Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei Taiwan Ind Technol Res Inst Hsinchu Taiwan

ISBN: (数字)9783319584843

ISBN: (纸本)9783319584843;9783319584836

Given the problems of gradual oil depletion and global warming, energy consumption has become a critical factor for energy-intensive sectors, especially the semiconductor, manufacturing, iron and steel, and aluminum industries. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical challenges. In response, we analyzed electricity consumption from the perspective of load profiling, which charts variation in electrical load during a specified period in order to track energy consumption. As a result, we proposed a time series data mining and analytic framework for electricity consumption analysis and pattern extraction by streaming data mining and machine learning techniques. We identified key factors to predict the state of the annealing furnace and detect abnormal patterns of the load profile of their electricity consumption. Our experimental results show that the dimension reduction method known as piecewise aggregate approximation can help to detect the state of the annealing furnace.

关键词： Energy consumption analysis Load profiling Piecewise aggregate approximation Time-series data mining

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