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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

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Deep learning based isolated Arabic scene character recognition

Deep learning based isolated Arabic scene character recognit...

引用

international workshop on Arabic Script Analysis and recognition (ASAR)

作者： Saad Bin Ahmed Saeeda Naz Muhammad Imran Razzak Rubiyah Yousaf King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia Malaysia Japan Institute of Information Technology (MJIIT) Universiti Teknologi Malaysia KualaLumpur Malaysia GGPGC No.1 Abbottabad Khyber Pakhtunkhua (KPK) Pakistan

ISBN: (纸本)9781509066292

The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively from images. In this work, we presented Arabic scene text recognition using Convolutional Neural Networks (ConvNets) as a deep learning classifier. As the scene text data is slanted and skewed, thus to deal with maximum variations, we employ five orientations with respect to single occurrence of a character. The training is formulated by keeping filter size 3 × 3 and 5 × 5 with stride value as 1 and 2. During text classification phase, we trained network with distinct learning rates. Our approach reported encouraging results on recognition of Arabic characters from segmented Arabic scene images.

关键词： Text recognition Feature extraction Image segmentation Image recognition Optical character recognition software Character recognition machine learning

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Automatic recognition of public transport trips from mobile device sensor data and transport infrastructure information 1st

引用

1st international workshop on Personal Analytics and Privacy, PAP 2017, Held in Conjunction with the European Conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2017

作者： Rinne, Mikko Bagheri, Mehrdad Tolvanen, Tuukka Hollmén, Jaakko Department of Computer Science Aalto University PO Box 15400 Aalto Espoo00076 Finland

ISBN: (纸本)9783319719696

Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by two means: (a) manually written details of each PT trip during the day, and (b) measurements using sensors of travellers’ mobile devices. The manual log is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-s interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information, and initial results from automatic recognition using a set of algorithms comparing measured trips with both live vehicle locations and static timetables. Improvement of correct recognitions is left as an ongoing challenge. © 2017, Springer international Publishi

关键词： Scheduling

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Unsupervised EEG Analysis for Automated Epileptic Seizure Detection 1

Unsupervised EEG Analysis for Automated Epileptic Seizure De...

引用

1st international workshop on pattern recognition (IWPR)

作者： Birjandtalab, Javad Pouyan, Maziyar Baran Nourani, Mehrdad Univ Texas Richardson Qual Life Technol Lab Richardson TX 75080 USA

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

关键词： EEG signals unsupervised seizure detection feature extraction data visualization

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Sentiment analysis of Arabic tweets using text mining techniques 1

Sentiment analysis of Arabic tweets using text mining techni...

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1st international workshop on pattern recognition (IWPR)

作者： Al-Horaibi, Lamia Khan, Muhammad Badruddin Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

Sentiment analysis has become a flourishing field of text mining and natural language processing. Sentiment analysis aims to determine whether the text is written to express positive, negative, or neutral emotions about a certain domain. Most sentiment analysis researchers focus on English texts, with very limited resources available for other complex languages, such as Arabic. In this study, the target was to develop an initial model that performs satisfactorily and measures Arabic Twitter sentiment by using machine learning approach, Naive Bayes and Decision Tree for classification algorithms. The datasets used contains more than 2,000 Arabic tweets collected from Twitter. We performed several experiments to check the performance of the two algorithms classifiers using different combinations of text-processing functions. We found that available facilities for Arabic text processing need to be made from scratch or improved to develop accurate classifiers. The small functionalities developed by us in a Python language environment helped improve the results and proved that sentiment analysis in the Arabic domain needs lot of work on the lexicon side.

关键词： Sentiment Analysis Twitter Arabic Python Naive Bayes Decision Tree machine learning NLTK Positive Negative Neutral

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mining Discriminative Class Codes for Multi-class Classification based on Minimizing Generalization Errors 1

Mining Discriminative Class Codes for Multi-class Classifica...

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1st international workshop on pattern recognition (IWPR)

作者： Eiadon, Mongkon Pipanmaekaporn, Luepol Kamonsantoroj, Suwatchai King Mongkuts Univ North Bangkok Dept Comp & Informat Sci Bangkok 10800 Thailand

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

Error Correcting Output Code (ECOC) has emerged as one of promising techniques for solving multi-class classification. In the ECOC framework, a multi-class problem is decomposed into several binary ones with a coding design scheme. Despite this, the suitable multi-class decomposition scheme is still ongoing research in machine learning. In this work, we propose a novel multi-class coding design method to mine the effective and compact class codes for multi-class classification. For a given n-class problem, this method decomposes the classes into subsets by embedding a structure of binary trees. We put forward a novel splitting criterion based on minimizing generalization errors across the classes. Then, a greedy search procedure is applied to explore the optimal tree structure for the problem domain. We run experiments on many multi-class UCI datasets. The experimental results show that our proposed method can achieve better classification performance than the common ECOC design methods.

关键词： Multi-class Classification Error Correcting Output Code Coding Design Binary learning problems

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Remote Sensing Image Object recognition Based on Convolutional Neural Network 1

Remote Sensing Image Object Recognition Based on Convolution...

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1st international Conference on Electronics Instrumentation & Information Systems (EIIS)

作者： Zhen, Yumei Liu, Huanyu Li, Junbao Hu, Cong Pan, Jeng-Shyang Harbin Inst Technol Dept Automat Test & Control Harbin 150080 Heilongjiang Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Fujian Peoples R China

ISBN: (纸本)9781538608432

The development of space remote sensing technology brings a lot of remote sensing image data. The traditional target detection method is difficult to adapt to the large amount of high-resolution remote sensing image data. It is necessary to find a way to automatically learn the most effective features from the image data, and to fully recover the correlation between the data. Based on the recognition of the typical targets in remote sensing image data, this paper proposes a method of remote sensing target recognition based on deep learning. In this paper, the pre-train method is introduced to improve the simulation of the model. The experiment of the test set proves the validity of the method.

关键词： deep learning object recognition pre-train

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Scene Text recognition Algorithm Based on Faster RCNN 1

Scene Text Recognition Algorithm Based on Faster RCNN

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1st international Conference on Electronics Instrumentation & Information Systems (EIIS)

作者： Wang, Boya Xu, Jianqing Li, Junbao Hu, Cong Pan, Jeng-Shyang Harbin Inst Technol Dept Automat Test & Control Harbin Heilongjiang Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Fujian Peoples R China

ISBN: (纸本)9781538608432

Industrial session of the natural scene in the text recognition technology has a great demand. The traditional optical character recognition technology (OCR) requires the text neat layout and neatness and background clean, and industrial production often fail to meet such standards. In this paper, a new text recognition algorithm based on deep learning is proposed for the existing problems of OCR technology. In this paper, a new method based on convolution neural network (Faster RCNN) is proposed to improve the correctness of text recognition. Compared with the conventional detection method, the correct rate of recognition based on Faster RCNN model can reach 90.4%, and the correctness rate is 88.9%. Experiments show that the recognition method in this paper is effective.

关键词： deep learning scene text recognition convolution neural network

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Extending framenet to machine learning domain 5

Extending framenet to machine learning domain

引用

5th Joint workshop on data mining and Knowledge Discovery meets Linked Open data and the 1st international workshop on Completing and Debugging the Semantic Web, Know@LOD 2016 and CoDeS 2016

作者： Jakubowski, Piotr Lawrynowicz, Agnieszka Institute of Computing Science Poznan University of Technology Poland

In recent years, several ontological resources have been proposed to model machine learning domain. However, they do not provide a direct link to linguistic data. In this paper, we propose a linguistic resource, a set of several semantic frames with associated annotated initial corpus in machine learning domain, we coined MLFrameNet. We have bootstrapped the process of (manual) frame creation by text mining on the set of 1293 articles from the machine learning Journal from about 100 volumes of the journal. It allowed us to find frequent occurences of words and bigrams serving as candidates for lexical units and frame elements. We bridge the gap between linguistics analysis and formal ontologies by typing the frame elements with semantic types from the DMOP domain ontology. The resulting resource is aimed to facilitate tasks such as knowledge extraction, question answering, summarization etc. in machine learning domain.

关键词： Ontology

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machine learning Model for Temporal pattern recognition

Machine Learning Model for Temporal Pattern Recognition

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IEEE EMBS international student Conference (ISC)

作者： Inibhunu, Catherine McGregor, Carolyn Univ Ontario Inst Technol 2000 Simcoe St North Oshawa ON L1H 7K4 Canada

ISBN: (纸本)9781509009350

Temporal abstraction and data mining are two research fields that have tried to synthesis time oriented data and bring out an understanding on the hidden relationships that may exist between time oriented events. In clinical settings, having the ability to know the hidden relationships on patient data as they unfold could help save a life by aiding in detection of conditions that are not obvious to clinicians and healthcare workers. Understanding the hidden patterns is a huge challenge due to the exponential search space unique to time-series data. In this paper, we propose a temporal pattern recognition model based on dimension reduction and similarity measures thereby maintaining the temporal nature of the raw data.

关键词： data mining

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The Neighborhood MCMC sampler for learning Bayesian networks 1

The Neighborhood MCMC sampler for learning Bayesian networks

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1st international workshop on pattern recognition (IWPR)

作者： Alyami, Salem A. Azad, A. K. M. Keith, Jonathan M. Monash Univ Sch Math Sci Clayton Vic 3800 Australia Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Riyadh Saudi Arabia

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

Getting stuck in local maxima is a problem that arises while learning Bayesian networks (BNs) structures. In this paper, we studied a recently proposed Markov chain Monte Carlo (MCMC) sampler, called the Neighbourhood sampler (NS), and examined how efficiently it can sample BNs when local maxima are present. We assume that a posterior distribution f (N, E\D) has been defined, where D represents data relevant to the inference, N and E are the sets of nodes and directed edges, respectively. We illustrate the new approach by sampling from such a distribution, and inferring BNs. The simulations conducted in this paper show that the new learning approach substantially avoids getting stuck in local modes of the distribution, and achieves a more rapid rate of convergence, compared to other common algorithms e.g. the MCMC Metropolis-Hastings sampler.

关键词： Directed acyclic graph structure inference local maxima graph space

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