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A new class of high-order neural networks with nonlinear decision boundaries

A new class of high-order neural networks with nonlinear dec...

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international conference on Neural Information Processing

作者： A. Bouzerdoum School of Engineering and Mathematics Edith Cowan University Joondalup WA Australia

Presents a class of high-order neural networks called shunting inhibitory artificial neural networks (SIANNs) for classification and function approximation tasks. In these networks, the basic synaptic interaction is of the shunting inhibitory type. Due to the nonlinearity mediated by shunting inhibition, these networks are capable of producing classifiers with complex nonlinear decision boundaries, ranging from simple hyperplanes to very complex nonlinear surfaces. therefore, developing efficient training algorithms for these networks will simplify the design of very powerful classifiers and function approximators. In this paper, we present a training method for a feedforward SIANN based on the backpropagation algorithm and on gradient descent.

关键词： Neural networks Artificial neural networks Neurons Function approximation pattern recognition Feedforward systems Backpropagation algorithms Mathematics Australia Algorithm design and analysis

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Smooth SVM research: A polynomial-based approach

Smooth SVM research: A polynomial-based approach

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international conference on Information, Communications and Signal Processing

作者： Yan-Feng Fan De-Xian Zhang Hua-Can He College of Information Science and Engineering Henan University of Technology Zhengzhou China College of Computer Science Northwest Polytechnical University Xi'an China

Classification can be promoted by using SVM to acquire hypersurface due to the direct induction of the support vectors. In traditional SVM solution algorithms, objective function is a strictly convex unconstrained optimization problem and it is not differentiable, so it can not use the most used optimization method to solve the problem. the undifferential model could be converted into a differential one by using polynomial to approximate the plus function x + . this paper gives the procedure of using cubic spline interpolation and Hermite interpolation method to deduce the quadratic polynomial smoothing the plus function x + , it also presents a new smooth technology using circular arc polynomial. the proposed approach is experimentally evaluated in three datasets that are benchmarks for data mining applications, leading to interesting results.

关键词： Support vector machines Polynomials Interpolation Support vector machine classification Optimization methods Spline Educational institutions Data mining Neural networks pattern recognition

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Improved Time Position Estimation of Manet UAV Routing Protocol

Improved Time Position Estimation of Manet UAV Routing Proto...

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IEEE international conference on Signal and Image Processing (ICSIP)

作者： Mesmin J. Mbyamm Kiki Ibrahim Iddi Haruna Yunusa School of Electronic and Information Engineering Beihang University Beijing China School of Pattern Recognition and Intelligent Systems Beihang University Beijing China

ISBN: (纸本)9781665446006

With routing protocols that are primarily concerned with location estimates, the most significant problem is that new neighbors are acquired slowly, which results in inefficient routing decisions. Because routing and forwarding would fail if the next-hop unmanned aerial vehicle (UAV) was no longer in sight. In this paper a system that makes use of unscented Kalman filter prediction in response to improve the next-hop position in real time in order to reduce the overall performance of route forwarding is proposed. It is being introduced to the classic GPRS protocol in order to facilitate the development of new routing protocols with OPNET. the new routing protocol demonstrates a greater rise in point-to-point packet delay and forwarding rate when compared to the original routing system.

关键词： Wireless communication Self-organizing networks Autonomous aerial vehicles Routing Routing protocols Real-time systems Mathematical models

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Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation

Spread Spectrum Signals Classification Based on the Wigner-V...

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Computer Information Systems and Industrial Management Applications (CISIM)

作者： Yuri Grishin Faculty of Electrical Engineering Bialystok Technical University Bialystok Poland

A spread spectrum signal recognition can be accomplished by exploiting the particular features of modulation presented in a received signal observed in presence of noise. these modulation features are the result of slight transmitter component variations and acts as an individual signature of a transmitter. the paper describes a spread spectrum signal classification algorithm based on using the Wigner-Ville distribution (WVD), noise reduction procedure with using a two- dimensional filter and the RBF neural network probability density function estimator which extracts the features vector used for the final signal classification. the numerical simulation results for the P4-coded signals are presented.

关键词： Spread spectrum communication pattern classification Neural networks Probability density function Transmitters Classification algorithms Noise reduction Filters Feature extraction Numerical simulation

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Extracting key challenges in achieving sobriety through shared subspace learning 12th

Extracting key challenges in achieving sobriety through shar...

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12th international conference on Advanced Data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, thin Rana, Santu Gupta, Sunil Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Alcohol abuse is quite common among all people without any age restrictions. the uncontrolled use of alcohol affects both the individual and society. Alcohol addiction leads to a huge increase in crime, suicide, health related problems and financial crisis. Research has shown that certain behavioral changes can be effective towards staying abstained. the analysis of behavioral changes of quitters and those who are at the beginning phase of quitting can be useful for reducing the issues related to alcohol addiction. Most of the conventional approaches are based on surveys and, therefore, expensive in both time and cost. Social media has lend itself as a source of large, diverse and unbiased data for analyzing social behaviors. Reddit is a social media platform where a large number of people communicate with each other. It has many different sub-groups called subreddits categorized based on the subject. We collected more than 40,000 self reported user’s data from a subreddit called ‘/r/stopdrinking’. We divide the data into two groups, short-term with abstinent days less than 30 and long-term abstainers with abstinent days greater than 365 based on badge days at the time of post submission. Common and discriminative topics are extracted from the data using JS-NMF, a shared subspace non-negative matrix factorization method. the validity of the extracted topics are demonstrated through predictive performance. © Springer international Publishing AG 2016.

关键词： Non-negative matrix factorization

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Understanding behavioral differences between short and long-term drinking abstainers from social media 12th

Understanding behavioral differences between short and long-...

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12th international conference on Advanced Data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, thin Gupta, Sunil Rana, Santu Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Drinking alcohol has high cost on society. the journey from being a regular drinker to a successful quitter may be a long and hard journey, fraught with the risk to relapse. Research has shown that certain behavioral changes can be effective towards staying abstained. Traditional way to conduct research on drinking abstainers uses questionnaire based approach to collect data from a curated group of people. However, it is an expensive approach in both cost and time and often results in small data with less diversity. Recently, social media has emerged as a rich data source. Reddit is one such social media platform that has a community (‘subreddit’) with an interest to quit drinking. the discussions among the group dates back to year 2011 and contain more than 40,000 posts. this large scale data is generated by users themselves and without being limited by any survey questionnaires. the most predictive factors from the features (unigrams, topics and LIWC) associated with short-term and long-term abstinence are identified using Lasso. It is seen that many common patterns manifest in unigrams, topics and LIWC. Whilst topics provided much richer associations between a group of words and the outcome, unigrams and LIWC are found to be good at finding highly predictive solo and psycho linguistically important words. Combining them we have found that many interesting patterns that are associated with the successful attempt made by the long-term abstainer, at the same time finding many of the common issues faced during the initial period of abstinence. © Springer international Publishing AG 2016.

关键词： Surveys

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Radar Phrase recognition Algorithm Based on New Word Discovery

Radar Phrase Recognition Algorithm Based on New Word Discove...

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international conference on Computer and Communications (ICCC)

作者： Liu Yang Weigang Zhu Shouye Lv Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Xi'an China UOSE Beijing China Beijing Institute of Remote Sensing Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Aiming at the problem that the previous recognition algorithms based on supervised learning are difficult to apply to non-cooperative multi-function radar, this paper proposes a radar phrase recognition algorithm based on new word discovery. the algorithm can effectively identify radar phrases without prior knowledge. It is verified by simulation experiments that with the increase of the input data size, the algorithm can overcome the problem of decreased phrase recognition rate caused by the loss of data quality. To a certain extent, the algorithm achieves the purpose of compensating for data “quality” with data “quantity”, and has good engineering applicability.

关键词： Data integrity conferences Supervised learning Radar Data models Natural language processing pattern recognition

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pattern recognition based Tasks and Achievements on Handwritten Bengali Character recognition

Pattern Recognition based Tasks and Achievements on Handwrit...

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international conference on Inventive Computation Technologies (ICICT)

作者： Apash Roy Debayani Ghosh Lovely Professional University Jalandhar Punjab India Chalmers University of Technology Sweden

ISBN: (数字)9781728185019

ISBN: (纸本)9781728185026

Bengali, is not only a major language in two states West Bengal, and Tripura of India, but it is the main language of an Entire country Bangladesh. Having such large application areas of Bengali language, and with the evolution of automation, machine learning, and trending technological development, it is also high time to make a complete product which can easily and efficiently recognize Bengali handwritten characters. Research in this area is attracting attention day by day, and there are the obvious reasons. Application area of such Bengali handwritten character recognition system is undoubtedly very high, but unfortunately, there is not such success in research, which can produce a complete operational system that can be commercialized or practically implemented in day to day life. the current work is a study of the different tasks to be done along with achievements throughout the last decade for different tasks in the field of handwritten Bengali character recognition.

关键词： Handwriting recognition Text recognition Machine learning Tools Feature extraction Character recognition Task analysis

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ICDAR 2019 CROHME + TFD: Competition on recognition of Handwritten Mathematical Expressions and Typeset Formula Detection

ICDAR 2019 CROHME + TFD: Competition on Recognition of Handw...

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international conference on Document Analysis and recognition

作者： Mahshad Mahdavi Richard Zanibbi Harold Mouchere Christian Viard-Gaudin Utpal Garain Document and Pattern Recognition Lab Rochester Institute of Technology Rochester NY USA University of Nantes Nantes France LS2N - UMR CNRS 6004 University of Nantes Nantes France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

We summarize the tasks, protocol, and outcome for the 6th Competition on recognition of Handwritten Mathematical Expressions (CROHME), which includes a new formula detection in document images task (+ TFD). For CROHME + TFD 2019, participants chose between two tasks for recognizing handwritten formulas from 1) online stroke data, or 2) images generated from the handwritten strokes. To compare LATEX strings and the labeled directed trees over strokes (label graphs) used in previous CROHMEs, we convert LATEX and stroke-based label graphs to label graphs defined over symbols (symbol-level label graphs, or symLG). More than thirty (33) participants registered for the competition, with nineteen (19) teams submitting results. the strongest formula recognition results were produced by the USTC-iFLYTEK research team, for both stroke-based (81%) and image-based (77%) input. For the new typeset formula detection task, the Samsung R&D Institute Ukraine (Team 2) obtained a very strong F-score (93%). System performance has improved since the last CROHME - still, the competition results suggest that recognition of handwritten formulae remains a difficult structural pattern recognition task.

关键词： Task analysis Handwriting recognition Tools Measurement Layout Image recognition

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Research on Partial Discharge pattern recognition of Transformer Bushings Based on KPCA and GTO-SVM Algorithm

Research on Partial Discharge Pattern Recognition of Transfo...

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international conference on Power and Energy Technology (ICPET)

作者： Qingdong Zhu Mengzhao Zhu Qihui Cui Jian Wang Zhaoliang Gu State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

Transformer bushings are critical components for the safe operation of power grids, and their failures can lead to significant risks. In order to improve the accuracy of diagnosing Partial Discharge (PD) types in transformer bushings, this paper proposes a PD pattern recognition algorithm based on Kernel Principal Component Analysis (KPCA) and Gorilla Troops Optimizer (GTO)-Support Vector Machine (SVM). Firstly, KPCA is used to reduce the dimensionality of high-frequency signals, and then the GTO algorithm is employed to optimize the selection of SVM parameters to improve the classification and recognition performance. Experimental results show that the proposed algorithm has a higher recognition rate in PD defect diagnosis. Compared with traditional SVM and BP neural network algorithms, it improves the overall recognition rate by 5% and 5.83% respectively, verifying its effectiveness and superiority in diagnosing transformer bushing defects. this study provides a new approach and method for the early defect diagnosis of transformer bushings, and has important engineering application value.

关键词： Support vector machines Partial discharges Accuracy Neural networks Transformers Insulators Vectors Classification algorithms pattern recognition Principal component analysis

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