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检索条件"主题词=K-Nearest Neighbors algorithm"

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Pedestrian Detection with Multiple Classifiers on Still Images 26

Pedestrian Detection with Multiple Classifiers on Still Imag...

引用

26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Cavdar, Caglar Ozek, Abdullah Bolat, Bulent NETAS Telekomunikasyon AS Elekt & Haberlesme Muhendisligi Bolumu Istanbul Turkey Yildiz Tekn Univ Elekt & Haberlesme Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781538615010

In this work, an algorithm that detects pedestrians in still images using different classifiers is presented. HOG, which is frequently used in pedestrian detection, and support vector machine (SVM), k nearest neighbors (kNN) and AdaBoost algorithms were used as descriptors. It is decided whether the image is pedestrian by looking at the result of three different classifiers. In order to demonstrate the effectiveness of the method, the system is trained using the INRIA data set and tested by using Penn Fudan Pedestrian Dataset which is different dataset. Experimental results show that the proposed method detects higher accuracy than pedestrian detection using a single classifier.

关键词： pedestiran detection HOG support vector machine k-nearest neighbors algorithm AdaBoost

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Sparse computation for large-scale data mining 2

Sparse computation for large-scale data mining

引用

IEEE International Conference on Big Data

作者： Hochbaum, Dorit S. Baumann, Philipp Univ Calif Berkeley Etcheverry Hall Berkeley CA 94720 USA

ISBN: (纸本)9781479956661

Several leading data mining and clustering algorithms rely on inputs in the form of pairwise similarities. Yet, since the number of potential pairwise similarities grows quadratically in the size of the data set, it is computationally prohibitive to apply such algorithms to large data sets. This paper addresses this challenge with a novel method of sparse computation that computes only the relevant similarities instead of the complete similarity matrix. The method employs an efficient algorithm that provides an "approximate Principal Component Analysis". In the low-dimensional space generated, the concept of grid neighborhoods is applied in order to identify groups of objects with potentially high similarity. Unlike known sparsification approaches that generate first the full set of pairwise similarities and thus take at least quadratic time, the sparse computation method generates only the relevant similarities. Sparse computation can be utilized in any data mining or clustering algorithm that requires pairwise similarities, such as the k-nearest neighbors algorithm or the spectral method. This approach is contrasted with that of grid-based clustering algorithms in that grid neighborhoods proximity is used only to determine the entries in the sparse similarity matrix, not to identify the clusters. Indeed objects can belong to the same grid neighborhood while ending up in different clusters, or conversely, belong to different neighborhoods yet get clustered jointly. The applicability of sparse computation for binary classification is demonstrated here for the recently devised supervised normalized cut (SNC). Our empirical results show that the approach achieves a significant reduction in the density of the similarity matrix, resulting in a substantial reduction in running time, while having a minimal effect (and often none) on accuracy as compared to inputs using a complete similarity matrix.

关键词： data mining grid computing pattern classification pattern clustering sparse matrices SNC approximate principal component analysis binary classification grid neighborhoods proximity grid-based clustering algorithms k-nearest neighbors algorithm large-scale data mining pairwise similarities sparse computation sparse similarity matrix sparsification spectral method supervised normalized cut Accuracy Approximation algorithms Approximation methods Data mining Principal component analysis Sparse matrices Vectors

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AN OPTIMIZED LICENSE PLATE RECOGNITION SYSTEM FOR COMPLEX SITUATIONS 5

AN OPTIMIZED LICENSE PLATE RECOGNITION SYSTEM FOR COMPLEX SI...

引用

5th International Conference on Audio, Language and Image Processing (ICALIP)

作者： Qiu, Jianing Zhu, Naida Wei, Yi Yu, XiaoQing Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China Shanghai Univ Inst Smart City Shanghai Peoples R China

ISBN: (纸本)9781509006540

This paper optimizes traditional license plate recognition system (LPRS) and for each original part with deficiencies, we give our own novel methods to refine them, such as integrating color and edge detection to increase the success rate of locating LPs as well as employing connected component analysis and vertical projection method alternatively to make segmentation more precise and efficient. For character recognition, we apply improved k-nearest neighbors algorithm and introduce some novel feature vectors to improve the accuracy of recognition. Our experimental results indicate that the optimized system has a high LP recognition rate with the accuracy of 96.75%.

关键词： license plate recognition color and edge detection connected component analysis vertical projection method k-nearest neighbors algorithm

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Skin Cancer Detection and Classification for Moles Using k-nearest Neighbor algorithm 18

Skin Cancer Detection and Classification for Moles Using K-N...

引用

5th International Conference on Bioinformatics Research and Applications (ICBRA)

作者： Linsangan, Noel B. Adtoon, Jetron J. Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines Univ Mindanao Coll Engn Educ Comp Engn Program Davao Philippines

ISBN: (纸本)9781450366113

The skin protects our body from heat and light of the sun and other threats. One of the illnesses that threaten the skin is the skin cancer. Skin cancer may start with an irregular shaped mole with size greater than a pencil eraser. This study focuses on the non-invasive approach in detecting and classifying skin cancer. Geometrical features of the moles suspected for skin cancer are extracted following the asymmetry, border, and diameter parameters of the ABCD-Rule of Dermoscopy. In particular, greatest and shortest diameter, irregularity index and equivalent diameter are the parameters loaded in the dataset for classification. Classification of mole images is done through k-nearest neighbors (k-NN) algorithm. The overall result showed 86.67% accuracy in determining the classification.

关键词： Moles Skin Cancer k-nearest neighbors algorithm Geometrical Features

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Modeling the Behavior of the Stock Market with an Artificial Immune System

Modeling the Behavior of the Stock Market with an Artificial...

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2010 IEEE World Congress on Computational Intelligence

作者： Butler, Matthew kazakov, Dimitar Univ York Artificial Intelligence Grp York YO10 5DD N Yorkshire England

ISBN: (纸本)9781424481262

This study analyzes the effectiveness of an Artificial Immune System (AIS) to model and predict the movements of the stock market. To aid in this research the AIS models are compared with a k-nearest neighbors (kNN) algorithm, an artificial neural network (ANN) and a benchmark market portfolio to compare simulated trading results. The analysis shows that the AIS produced overall accuracy results of 67% over a 20 year test period and that the increased complexity of the model was warranted by the statistically significant superior results when compared to the simpler instance-based approach of kNN. The accuracy results were comparable to those obtained from training the ANN and the trading results outperformed the market benchmark, providing evidence that the stock market had a degree of predictability during the time period of 1989-2008. In general the practice of using the natural immune system to inspire a learning algorithm has been established as a viable alternative to modeling the stock market when implementing a supervised learning approach.

关键词： AIS model artificial immune system artificial immune systems artificial neural network benchmark market portfolio benchmark testing economic forecasting k-nearest neighbors algorithm learning algorithm natural immune system stock market prediction stock markets supervised learning approach

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Email Classification Using Supervised Learning algorithms

Email Classification Using Supervised Learning Algorithms

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International Conference on Advanced Computing and Intelligent Engineering (ICACIE)

作者： Bhadra, Akshay Hitawala, Saifuddin Modi, Ruchit Salunkhe, Suraj Veermata Jijabai Technol Inst IT Dept Bombay 400019 Maharashtra India

ISBN: (纸本)9789811068751;9789811068744

In the world of Internet today, huge amount of data is transferred between computers in the form of emails. Consequently, it is getting difficult to sort the important emails manually from the unimportant ones. Email classification has been extensively studied and researched in the past but most of the research has been in the field of spam detection and filtering. This paper focuses on classifying emails into custom folders that are relevant to the user. We have used two different approaches here-Naive Bayes classifier and k-nearest neighbors algorithm. The Naive Bayes classifier is based on a probabilistic model, while the k-nearest neighbors algorithm is based on a similarity measure with the training emails. We propose the method of using these two approaches in email classification, analyze the performance of these algorithms, and compare their results. Then, we propose some future work for further optimization and better efficiency.

关键词： Text classification k-nearest neighbors algorithm Naive bayes classifier

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Comparative Analysis of algorithms for student characteristics classification using a Methodological Framework 6

Comparative Analysis of algorithms for student characteristi...

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6th International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Troussas, Christos Virvou, Maria Mesaretzidis, Spyridon Univ Piraeus Dept Informat Piraeus Greece Cardiff Metropolitan Univ Dept Comp Cardiff S Glam Wales

ISBN: (纸本)9781467393119

Data clustering and mining have evolved into a major research topic. The techniques used to cluster data, are applicable in a number of subjects. The education arena offers a fertile ground for data mining applications, since there are multiple sources of data and diverse interest groups. Thus, modeling student performance, can be a great tool for both educators as well as students, in order to make correct adjustments to the curriculum as well as the teaching methods. As a testbed for this study, four classification algorithms were chosen for comparison, namely the k-means algorithm, the nearest neighbors algorithm, Support Vector Machines and the Naive Bayes Classifier. Though research algorithmic on performance has already being performed, a concrete conclusion has yet to be drawn. Therefore the focus of this study is to determine any differences and similarities that these algorithms may have on the data mining of student characteristics. To achieve this, several classification models were used, while a regression model was also introduced.

关键词： data mining clustering algorithms tutoring systems k-means algorithm k-nearest neighbors algorithm support vector machines naive bayes classifier language learning

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Two component data representation using piecewise approximation and specific points for IoT 9

Two component data representation using piecewise approximat...

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9th International Conference on Information and Communication Technology Convergence (ICTC)

作者： Park, Hyunjae Choi, Young-June Ajou Univ Comp Engn Suwon South Korea

ISBN: (纸本)9781538650417

In these days, concept of IoT is widely spread from individual devices to large industrial systems. However, data handling from IoT devices is on developing stage. Delivering large amount of data to server which mainly operates data analysis is required and it becomes a burden to network. Our work has focused on reducing volume of data to transmit with data approximation in the domain of time series data. We suggest a data representation method which separates data into specific data point and piecewise approximation values. In addition, we show the difference between raw data and reduced data in distance and in time series classification accuracy.

关键词： IoT Data compression Approximation k-nearest neighbors algorithm Dynamic time warping Time series

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Methods and algorithms of Data and Machine Learning usage in Management Decision Making Support Systems 8

Methods and Algorithms of Data and Machine Learning usage in...

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8th Mediterranean Conference on Embedded Computing (MECO)

作者： Savenkov, Pavel A. Ivutin, Alexey N. Tula State Univ Dept Comp Technol Tula Russia

ISBN: (纸本)9781728117393

The review of methods of data analysis and also methods of machine learning is carried out in this article. Application of machine learning and the analysis in implementation of decision making support system methods is offered. It allows to achieve efficient data analysis, to find difficult correlations in them that finally will allow to make the weighed management decisions

关键词： Big Data Data science software Information system Machine learning unstructured data dynamic model neural network k-nearest neighbors algorithm

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A Global Decoding Strategy with a Reduced-Reference Metric Designed for the Wireless Transmission of JPWL 19th

A Global Decoding Strategy with a Reduced-Reference Metric D...

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19th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS)

作者： Xie, Xinwen Carre, Philippe Perrine, Clency Pousset, Yannis Wu, Jianhua Zhou, Nanrun Univ Poitiers XLIM Inst F-86962 Futuroscope France Nanchang Univ Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China Jiujiang Univ Dept Elect Engn Jiujiang 332005 Peoples R China

ISBN: (纸本)9783030014490;9783030014483

A new global decoding strategy with Reduced-Reference (RR) metric is proposed to improve the Quality of Experience (QoE) in a wireless transmission context. The RR metric (FMRP) utilizes the magnitude and the relative phase information in the complex wavelet domain as the evaluation features. It determines the number of decoder layers to achieve the goal of evaluating the image in a consistent way with the Human Visual system (HVS). To evaluate the performance of the decoding strategy, we collected some distorted images in realistic channel attacks and recruited volunteers to do a large psychovisual test. The distorted images and the classification data of voluntary assessors are integrated into a database which is in a realistic wireless channel context quite different from the classic database. Experimental studies confirm that the decoding strategy is effective and improves the QoE while ensuring the Quality of Service (QoS).

关键词： Reduced-reference image quality metric Quality of experience Decoding strategy Support vector machine k-nearest neighbors algorithm

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