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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3281 条记录，以下是2411-2420 订阅

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My camera can see through fences: A deep learning approach for image de-fencing

My camera can see through fences: A deep learning approach f...

引用

Asian conference on pattern recognition (ACPR)

作者： Sankaraganesh Jonna Krishna K. Nakka Rajiv R. Sahay School of Information Technology Indian Institute of Technology Kharagpur Kharagpur India Department of Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9781479961016

In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. However, sometimes the amateur photographer is hindered by fences in the scene which have to be removed after the image has been captured. Conventional approaches to image de-fencing suffer from inaccurate and non-robust fence detection apart from being limited to processing images of only static occluded scenes. In this paper, we propose a semi-automated de-fencing algorithm using a video of the dynamic scene. We use convolutional neural networks for detecting fence pixels. We provide qualitative as well as quantitative comparison results with existing lattice detection algorithms on the existing PSU NRT data set [1] and a proposed challenging fenced image dataset. The inverse problem offence removal is solved using split Bregman technique assuming total variation of the de-fenced image as the regularization constraint.

关键词： Heuristic algorithms Cameras machine learning Dynamics Training databases Robustness

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NLP based sentiment analysis on Twitter data using ensemble classifiers

NLP based sentiment analysis on Twitter data using ensemble ...

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international conference on Signal Processing, Communication and Networking (ICSCN)

作者： Monisha Kanakaraj Ram Mohana Reddy Guddeti Dept. of Information Technology National Institute of Technology Karnataka Mangalore India National Institute of Technology Calicut Calicut Kerala IN

Most sentiment analysis systems use bag-of-words approach for mining sentiments from the online reviews and social media data. Rather considering the whole sentence/ paragraph for analysis, the bag-of-words approach considers only individual words and their count as the feature vectors. This may mislead the classification algorithm especially when used for problems like sentiment classification. Traditional machine learning algorithms like Naive Bayes, Maximum Entropy, SVM etc. are widely used to solve the classification problems. These machine learning algorithms often suffer from biasness towards a particular class. In this paper, we propose Natural Language (NLP) based approach to enhance the sentiment classification by adding semantics in feature vectors and thereby using ensemble methods for classification. Adding semantically similar words and context-sense identities to the feature vectors will increase the accuracy of prediction. Experiments conducted demonstrate that the semantics based feature vector with ensemble classifier outperforms the traditional bag-of-words approach with single machine learning classifier by 3-5%.

关键词： machine learning algorithms Twitter Training Sentiment analysis Semantics Signal processing algorithms Support vector machines

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Development of Improved ID3 Algorithm with TkNN Clustering Using Car dataset

Development of Improved ID3 Algorithm with TkNN Clustering U...

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The 3rd international conference on Advances in Engineering Sciences & Applied Mathematics (ICAESAM’2015, UK)

作者： M.Jayakameswaraiah S.Ramakrishna Department of Computer Science Sri Venkateswara University

data mining involves an integration of techniques from multiple disciplines such as database technology, statistics, machine learning. The objective of this research undertaking was to explore the possible application of data mining technology for mining training dataset. In this paper, Improved ID3 and Tk NN Clustering decision making, classification with clustering method is used to build an effective decision making approach for capable performance. And also we proposed Improved ID3 with Tk NN algorithm for best car market analysis. We have executed in Weka Tool with Java code. We analyzed the graphical performance analysis with Classes to Clusters evaluation, purchase, safety, luggage booting, persons(seating capacity), doors, maintenance and buying attri butes of customer's requirements for unacceptable/acceptable/good/very good ratings of a car to purchase.

关键词： data mining Classification KNN ID3 Algorithm Improved ID3 with TkNN Clustering

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Blood donor's safety using data mining

Blood donor's safety using data mining

引用

international conference on Green Computing and Internet of Things (ICGCIoT)

作者： Rohini Patil Madhu Poi Pooja Pawar Tejashree Patil Namrata Ghuse Dept. of Computer Science SITRC Nasik India

ISBN: (纸本)9781467379113

Blood is most essential constituent in human body. Blood centers collect, process and transport blood to hospitals and other health care centers. Sometimes blood is collected directly from blood donors rather than taking it from blood bank. Nowadays donor safety is major issue as blood is collected from different donors. Small amount of donors face reactions after donating blood and if not treated on time then it may lead to serious injury. The reactions that occur in the donor during blood donation at different hospitals and blood banks is collected, organized and analyzed by the DonorHART tool [1, 3]. The donor reactions information are captured and analyzed by DonorHART system[1]. The tool also monitors and researches the risks involved for donors at the time of blood donation or after the donation process. data mining makes an effort to reveal the patterns in data that are difficult to detect and recognize with automatic pattern recognition. It is analyzing of data and then summarizing it into different form which is called as information and is taken from different databases. The proposed system is a web based application which helps to reduce the human mistakes or errors and different techniques can be applied for blood type classification, diagnosing diabetic symptoms, classifying blood type and identifying different donors reaction and apply preventive measures against them.

关键词： Blood data mining Classification algorithms Safety Cloud computing pattern matching machine learning algorithms

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A Study on Apparent Age Estimation

A Study on Apparent Age Estimation

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international conference on Computer Vision Workshops (ICCV Workshops)

作者： Yu Zhu Yan Li Guowang Mu Guodong Guo Lane Department of CSEE West Virginia University Morgantown WV USA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from?the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age labels are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are?not many face images available with annotated age labels. Further, the annotated age labels for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age labels to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age labels, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.

关键词： Estimation Face databases Training Neural networks data models Computer architecture

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pattern recognition with Non-Euclidean Similarities 3

Pattern Recognition with Non-Euclidean Similarities

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3rd international conference on Man-machine Interactions (ICMMI)

作者： Hancock, Edwin R. Xu, Eliza Wilson, Richard C. Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

ISBN: (纸本)9783319023090

Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in pattern recognition. One of the problems encountered in the analysis of such data, is that the dissimilarities are rarely Euclidean, while statistical learning algorithms often rely on Euclidean distances. Such non-Euclidean dissimilarities are often corrected or imposed geometry via embedding. This talk reviews and and extends the field of analysing non-Euclidean dissimilarity data.

关键词： non-Euclidean pairwise data similarity metric Ricci flow embedding

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learning with kernel random field and linear SVM

Learning with kernel random field and linear SVM

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Takahashi, Haruhisa University of Electro-Communications Choufushi Tokyo 182-8585 Japan

ISBN: (纸本)9789897580185

Deep learning methods, which include feature extraction in the training process, are achieving success in pattern recognition and machine learning fields but require huge parameter setting, and need the selection from various methods. On the contrary, Support Vector machines (SVMs) have been popularly used in these fields in light of the simple algorithm and solid reasons based on the learning theory. However, it is difficult to improve recognition performance in SVMs beyond a certain level of capacity, in that higher dimensional feature space can only assure the linear separability of data as opposed to separation of the data manifolds themselves. We propose a new framework of kernel machine that generates essentially linearly separable kernel features. Our method utilizes pretraining process based on a kernel generative model and the mean field Fisher score with a higher-order autocorrelation kernel. Thus derived features are to be separated by a liner SVM, which exhibits far better generalization performance than any kernel-based SVMs. We show the experiments on the face detection using the appearance based approach, and that our method can attain comparable results with the state-of-the-art face detection methods based on AdaBoost, SURF, and cascade despite of smaller data size and no preprocessing. Copyright © 2014 SCITEPRESS.

关键词： Support vector machines

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Fast pattern recognition and Deep learning Using Multi-Rooted Binary Decision Diagrams

Fast Pattern Recognition and Deep Learning Using Multi-Roote...

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10th international conference on machine learning and data mining (MLDM)

作者： Bugaychenko, Dmitry Zubarevich, Dmitry St Petersburg State Univ St Petersburg 199034 Russia

ISBN: (纸本)9783319089799;9783319089782

Binary decision diagrams (BDD) is a compact and efficient representation of Boolean functions with extensions available for sets and finite-valued functions. The key feature of the BDD is an ability to employ internal structure (not necessary known upfront) of an object being modelled in order to provide a compact in-memory representation. In this paper we propose application of the BDD for machine learning as a tool for fast general pattern recognition. Multiple BDDs are used to capture a sets of training samples (patterns) and to estimate the similarity of a given test sample with the memorized training sets. Then, having multiple similarity estimates further analysis is done using additional layer of BDDs or common machine learning techniques. We describe training algorithms for BDDs (supervised, unsupervised and combined), an approach for constructing multi-layered networks combining BDDs with traditional artificial neurons and present experimental results for handwritten digits recognition on the MNIST dataset.

关键词： pattern recognition binary decision diagrams

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Improving kernel grower methods using ellipsoidal support vector data description

Improving kernel grower methods using ellipsoidal support ve...

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3rd international conference on pattern recognition Applications and Methods, ICPRAM 2014

作者： Hechmi, Sabra Slimene, Alya Zagrouba, Ezzeddine Higher Institute of Computer Science Tunis El Manar University 2 Rue Abou Rayhane Bayrouni 2080 Ariana Tunisia

ISBN: (纸本)9789897580185

In these recent years, kernel methods have gained a considerable interest in many areas of machine learning. This work investigates the ability of kernel clustering methods to deal with one of the meaningful problem of computer vision namely image segmentation task. In this context, we propose a novel kernel method based on an Ellipsoidal Support Vector data Description ESVDD. Experiments conducted on a selected synthetic data sets and on Berkeley image segmentation benchmark show that our approach significantly outperforms state-of-the-art kernel methods. Copyright © 2014 SCITEPRESS.

关键词： Image segmentation

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Application of Multidimensional data Visualization in Creation of pattern recognition Systems 3

Application of Multidimensional Data Visualization in Creati...

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3rd international conference on Man-machine Interactions (ICMMI)

作者： Jamroz, Dariusz AGH Univ Sci & Technol PL-30059 Krakow Poland

ISBN: (纸本)9783319023090

The paper presents the application of multidimensional data visualization to obtain the views of 5-dimensional space of features created by recognition of printed characters. On the basis of these views it was stated that the features chosen to construction of features space are sufficient to correct recognition process. This is the significant help by constructing the recognition systems because the correct selection of objects properties on the basis of which the recognition should occur is one of the hardest stages.

关键词： multidimensional visualization pattern recognition

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