Filters








37,834 Hits in 6.7 sec

Unsupervised Parameter Estimation for One-Class Support Vector Machines [chapter]

Zahra Ghafoori, Sutharshan Rajasegarar, Sarah M. Erfani, Shanika Karunasekera, Christopher A. Leckie
2016 Lecture Notes in Computer Science  
Although the hyper-plane based One-Class Support Vector Machine (OCSVM) and the hyper-spherical based Support Vector Data Description (SVDD) algorithms have been shown to be very effective in detecting  ...  In this paper, we propose two unsupervised methods for estimating the optimal parameter settings to train OCSVM and SVDD models, based on analysing the structure of the data.  ...  One-class Support Vector Machines The OCSVM or ν-SVM [14] algorithm is a semi-parametric one-class classification method that finds a boundary around dense areas comprising the normal data [7] .  ... 
doi:10.1007/978-3-319-31750-2_15 fatcat:wfqq3ghpl5an5j5agpkwbhmshq

Unsupervised and Semi-supervised Lagrangian Support Vector Machines [chapter]

Kun Zhao, Ying-Jie Tian, Nai-Yang Deng
2007 Lecture Notes in Computer Science  
Recently two-class unsupervised and semisupervised classification problems based on Bounded C-Support Vector Machines and Bounded ν-Support Vector Machines are relaxed to semidefinite programming[4] [11  ...  Support Vector Machines have been a dominant learning technique for almost ten years, moreover they have been applied to supervised learning problems.  ...  De Bie and Cristanini relax two-class transduction problem to semi-definite programming based on transductive Support Vector Machines [5] .  ... 
doi:10.1007/978-3-540-72588-6_140 fatcat:abtclimzbfer7cot7zlh4yfth4

Implications of Experiment Set-Ups for Residential Water End-Use Classification

Nora Gourmelon, Siming Bayer, Michael Mayle, Guy Bach, Christian Bebber, Christophe Munck, Christoph Sosna, Andreas Maier
2021 Water  
Previously, both supervised and unsupervised machine learning (ML) techniques are employed, demonstrating accurate classification results on particular datasets.  ...  Subsequently, unsupervised clustering methods, such as dynamic time warping, k-means, DBSCAN, OPTICS and Hough transform, are compared to supervised methods based on SVM.  ...  Therefore, the support vectors lie within the margin boundaries. The maximal margin hyperplane can be described solely based on the support vectors, making SVMs memory efficient.  ... 
doi:10.3390/w13020236 fatcat:v3xrvyydbra7jjwm4ihm25f54m

Machine Learning Techniques for Anomaly Detection: An Overview

Salima Omar, Asri Ngadi, Hamid H. Jebur
2013 International Journal of Computer Applications  
This paper presents an overview of research directions for applying supervised and unsupervised methods for managing the problem of anomaly detection.  ...  Intrusion detection has gain a broad attention and become a fertile field for several researches, and still being the subject of widespread interest by researchers.  ...  One -Class Support Vector Machine (OCSVM) The one-class support vector machine is a very specified sample of a support vector machine which is geared for anomaly detection.  ... 
doi:10.5120/13715-1478 fatcat:rjkjycwknbclzgvfjp24cg5ohi

A Study on Advantages of Data Mining Classification Techniques

O. Yamini, Prof. S. Ramakrishna
2015 International Journal of Engineering Research and  
This paper provides different classification techniques analogous as Decision tree Induction, Bayesian Classification, Neural networks, Support Vector Machines.  ...  The success of any organization confide in imposingly on the orbit to which the data acquired from business operations is utilized.  ...  Support Vector Machine SVM, a powerful machine Support Vector Machine (SVM) was first proposed by Vapnik and has since attracted a potency of interest in the machine learning research community [10]  ... 
doi:10.17577/ijertv4is090815 fatcat:q2zzmn7t7jfbfnsinliogf25ne

DEVELOPMENT OF THE METHOD OF UNSUPERVISED TRAINING OF CONVOLUTIONAL NEURAL NETWORKS BASED ON NEURAL GAS MODIFICATION

Viacheslav Moskalenko
2017 Technology Transfer fundamental principles and innovative technical solutions  
Acknowledgments The work is supported in the framework of the research work of ДР № 0117U003934 "Intellectual autonomous on-board system of an unmanned aerial vehicle for identification of objects on the  ...  At the same time, the use of information efficiency criteria and population-based algorithms for searching for optimal parameters of functioning makes it possible to realize automatic regularization of  ...  the feature extractor's filters and parameters of the unsupervised training algorithm.  ... 
doi:10.21303/2585-6847.2017.00469 fatcat:6qjebhai2vgsxifvcwnevhlwpu

Using Unsupervised Analysis to Constrain Generalization Bounds for Support Vector Classifiers

S. Decherchi, S. Ridella, R. Zunino, P. Gastaldo, D. Anguita
2010 IEEE Transactions on Neural Networks  
The Maximal Discrepancy approach is a very promising technique for model selection for Support Vector Machines (SVM), and estimates a classifier's generalization performance by multiple training cycles  ...  When one estimates the generalization error, one uses an unsupervised reference to constrain the complexity of the learning machine, thereby possibly decreasing sharply the number of admissible hypothesis  ...  Fabio Rivieccio, for his preliminary studies on this research, and the fruitful, constructive suggestions by the anonymous reviewers.  ... 
doi:10.1109/tnn.2009.2038695 pmid:20123572 fatcat:icqpjgrjnban5hb5ec3n5fj2de

Role of Support Vector Machine Fuzzy KMeans and Naive Bayes Classification in Intrusion Detection System

Aman Mudgal
2015 International Journal on Recent and Innovation Trends in Computing and Communication  
For detecting any Intrusion in a network or system there are number of techniques which are used and can be developed to prevent.  ...  Support Vector Machine Support Vector Machines (SVM's) are a relatively new learning method used for binary classification.  ...  Linear Programming Machine and Support Vector Machine -Linear Programming Machine (LPM) and Support Vector Machine (SVM) construct a hyper plane of the minimal norm which separates the two classes of training  ... 
doi:10.17762/ijritcc2321-8169.150346 fatcat:apv4ccpz35gtpnlah3t63ub3vi

Model and Training Methods of Autonomous Navigation System for Compact Drones

Viacheslav Moskalenko, Alona Moskalenko, Artem Korobov, Olha Boiko, Serhii Martynenko, Oleksandr Borovenskyi
2018 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP)  
The paper presents a novel model of convolutional neural network for visual feature extraction, support vector machine for position prediction and information-extreme classifier for obstacle prediction  ...  The complex criterion for choosing parameter of feature extractor model is considered.  ...  ACKNOWLEDGMENT The work was performed in the laboratory of intellectual systems of the computer science department at Sumy State University with the financial support of the Ministry of Education and Science  ... 
doi:10.1109/dsmp.2018.8478521 fatcat:qhmcpl6f2nhvdc2ev7jkca2kvq

Efficient recurrent local search strategies for semi- and unsupervised regularized least-squares classification

Fabian Gieseke, Oliver Kramer, Antti Airola, Tapio Pahikkala
2012 Evolutionary Intelligence  
For this reason, the concept of support vector machines has also been extended to semi-and unsupervised settings: In the unsupervised case, one aims at finding a partition of the data into two classes  ...  One prominent approach to address such tasks are support vector machines which aim at finding a hyperplane separating two classes well such that the induced distance between the hyperplane and the patterns  ...  The Cholesky decomposition for a m × m-matrix can be obtained in O(m 3 ) time (in practice and up to machine precision, see Golub and Van Loan (1989) pp. 141-145).  ... 
doi:10.1007/s12065-012-0068-5 fatcat:ahgubi7cjbcl7bhvtxzquyldgi

Text Classification Techniques: A Literature Review

2018 Interdisciplinary Journal of Information, Knowledge, and Management  
Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery.  ...  It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making.  ...  Support vector machine The Support Vector Machine (SVM) algorithm is one of the supervised machine learning algorithms that is employed for various classification problems (Demidova, Klyueva, Sokolova  ... 
doi:10.28945/4066 fatcat:6dio5bpajjf77lkrs7xdtciveu

A Review of Clustering Technique Based on Different Optimization Function Using for Selection of Center Point

Kavita Firke
2017 International Journal for Research in Applied Science and Engineering Technology  
The process of clustering basically group the data based on feature attribute of data. the selection of features attribute of data based on the process of iteration.  ...  In this paper present the review of clustering technique for automatic validation and cluster center selection.  ...  Author describe, Support vector machine is well recognized method for data classification. For the process of support vector machine evaluation of new feature during classification is major problem.  ... 
doi:10.22214/ijraset.2017.3008 fatcat:3sjoz5ti3bg5voyxusyr5n22wu

Machine Learning [chapter]

Mariette Awad, Rahul Khanna
2015 Efficient Learning Machines  
The process of generalization requires classifiers that input discrete or continuous feature vectors and output a class.  ...  Alan Turing's seminal paper (Turing 1950) introduced a benchmark standard for demonstrating machine intelligence, such that a machine has to be intelligent and responsive in a manner that cannot be differentiated  ...  Support Vector Machines Support vector machines (SVMs) are supervised learning methods that analyze data and recognize patterns.  ... 
doi:10.1007/978-1-4302-5990-9_1 fatcat:5hwjpdcxb5ctlavhtw3iql2oei

Comparative Study of Microarray Based Disease Prediction - A Survey

T. Sneka, K. Palanivel
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
High dimensional data is one of the major issues caused while handling microarray. Also because of this issue, possibilities of redundant, irrelevant and noisy data may occur.  ...  This survey observes some various techniques of classification, clustering of genes and feature selection methods such as supervised, unsupervised and semi-supervised methods.  ...  For example, the engineer may choose to use support vector machines or decision trees.• Complete the design. Run the learning algorithm on the gathered training set.  ... 
doi:10.32628/cseit195435 fatcat:jpbopg3mmvczpm4q3bqcdk55pq

A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data [chapter]

Justin H. Le, Ali Pour Yazdanpanah, Emma E. Regentova, Venkatesan Muthukumar
2015 Lecture Notes in Computer Science  
The support vector machine (SVM) is used as a baseline to determine that the proposed method is feasible, offering consistently high classification accuracies in comparison.  ...  In this paper, we investigate deep learning architectures (DLAs), whose popularity has grown recently due to the discovery of efficient algorithms to train them, one of which, unsupervised pre-training  ...  This research was supported by NASA EPSCoR under cooperative agreement No. NNX10AR89A.  ... 
doi:10.1007/978-3-319-27857-5_61 fatcat:e2dweu3nhfbvbkyp6bffp2v63i
« Previous Showing results 1 — 15 out of 37,834 results