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Over-Complete Wavelet Approximation of a Support Vector Machine for Efficient Classification [chapter]

Matthias Rätsch, Sami Romdhani, Gerd Teschke, Thomas Vetter
2005 Lecture Notes in Computer Science  
We show that our algorithm provides, for a comparable accuracy, a 15 fold speed-up over the Reduced Support Vector Machine and a 530 fold speed-up over the Support Vector Machine, enabling face detection  ...  This over-complete transformation finds the optimal approximation of the Support Vectors by a set of rectangles with constant graylevel values (enabling an Integral Image based evaluation).  ...  Over-Complete Wavelet Approximated Support Vector Machine Support Vector Machines (SVM), used as classifiers, are now well-known for their good generalisation capabilities.  ... 
doi:10.1007/11550518_44 fatcat:m5j3lv76b5aglgdu2462b6kq6u

Wavelet Frame Accelerated Reduced Support Vector Machines

Matthias Ratsch, Gerd Teschke, Sami Romdhani, Thomas Vetter
2008 IEEE Transactions on Image Processing  
This is achieved by an Over-Complete Wavelet Transform that finds the optimal approximation of the Support Vectors.  ...  The obtained classifier is fast, since a Haar wavelet approximation of the Support Vectors is used, enabling efficient Integral Image based kernel evaluations.  ...  ACKNOWLEDGMENT The authors would like to thank the group of Hans Burkhardt of the University of Freiburg and particularly Ralf Jüngling for providing the face images used in the experiments.  ... 
doi:10.1109/tip.2008.2001393 pmid:19004715 fatcat:mwbqlcspmbegbjfdgytsuff4zq

Wavelet Reduced Support Vector Regression for Efficient and Robust Head Pose Estimation

Matthias Ratsch, Philip Quick, Patrik Huber, Tatjana Frank, Thomas Vetter
2012 2012 Ninth Conference on Computer and Robot Vision  
The Wavelet-Approximated Reduced Vector Machine classifiers for face and facial feature point detection are extended to regression for efficient and robust head pose estimation.  ...  In this paper, we introduce concepts to reduce the computational complexity of regression, which are successfully used for Support Vector Machines.  ...  ACKNOWLEDGMENTS The authors would like to thank Michael Fischer for training WVMs and his work on WVM-trees.  ... 
doi:10.1109/crv.2012.41 dblp:conf/crv/RatschQHFV12 fatcat:y2bbk6pmofaojnshgoh3rsfony

SVM parameters tuning with quantum particles swarm optimization

Zhiyong Luo, Wenfeng Zhang, Yuxia Li, Min Xiang
2008 2008 IEEE Conference on Cybernetics and Intelligent Systems  
The parameters of least squares support vector machines (LS-SVM) can be adjusted using QPSO. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel.  ...  Common used parameters selection method for support vector machines (SVM) is cross-validation, which is complicated calculation and takes a very long time.  ...  INTRODUCTION As a machine learning method, support vector machines (SVM) originally introduced by Vapnik [1] within the area of the statistical theory and structural risk minimization has emerged as  ... 
doi:10.1109/iccis.2008.4670970 fatcat:nq4rq4ftq5ettbtws4e4trdlte

A Novel Scheme to Classify EHG Signal for Term and Pre-term Pregnancy Analysis

Sindhiya Arora, Girisha Garg
2012 International Journal of Computer Applications  
Classification is done using Support Vector Machines (SVM) by dividing the data into test and training sets. It is validated on a well known benchmark database from Physionet Database.  ...  This paper proposes a four-level decomposition of Electrohysterography (EHG) signals using Discrete Wavelet Transform (DWT) based on pyramid algorithm to obtain the final feature vector matrix.  ...  A classifier plays a very important role and thus it should accurately separate the data into the two groups. For this work Support Vector Machines (SVM) is used for classification purposes.  ... 
doi:10.5120/8144-1928 fatcat:3w2szdr4p5gh7hz75imzn5x3za

EARLY SEIZURE DETECTION TECHNIQUES: A Review

Manpreet Kaur, Neelam Rup Prakash, Parveen Kalra
2018 Indian Journal of Science and Technology  
It was observed that the technique employing DT-CWT as a feature set and Support Vector Machine as a classifier resulted in maximum classification accuracy of 100% and 0 false alarm rate.  ...  For the classification, different classifiers like RNN, Artificial Neural Network, Modified Neural Network and Support Vector Machine were used.  ...  SVM Support Vector Machine is used for classification of the data of two different classes. It finds the hyperplane for segregation of the data vectors.  ... 
doi:10.17485/ijst/2018/v11i10/99176 fatcat:myt4vcqha5htxjbs35x2jgmgyu

Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine

Vasudha Harlalka, Viraj Pradip Puntambekar, Kalugotla Raviteja, P. Mahalakshmi
2018 International Journal of Engineering & Technology  
These features are fed to Linear Support Vector Machine (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved.  ...  The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT).  ...  These datasets-A, B, C, D, E-is used for entropy values calculations. The values of ShanEn and LogEn is fed to Support Vector Machine (SVM) for classification into healthy, interictal and ictal.  ... 
doi:10.14419/ijet.v7i4.10.26630 fatcat:dm37cdwuf5d2rlkcfmrzkhtz2i

Wavelet Energy based Neural Fuzzy Model for Automatic Motor Imagery Classification

Girisha Garg, Shruti Suri, Rachit Garg, Vijander Singh
2011 International Journal of Computer Applications  
The classification accuracy achieved 93.5% in the course of testing on the data from subject. Support Vector Machine is also used to compare the performance with ANFIS.  ...  Firstly, wavelet transform and energy of the decomposed signal is used to obtain the final feature vector matrix. Secondly, the feature data is classified using ANFIS. .  ...  Support Vector Machine Classifier A support vector machine (SVM) is a new machine learning method that analyzes data and recognize patterns, used for classification and regression analysis.  ... 
doi:10.5120/3403-4745 fatcat:kesvzv4fzjcodoj5q6djkmod24

Tree Based Wavelet Transform and DAG SVM for Seizure Detection

AS Muthanantha Murugavel
2012 Signal & Image Processing An International Journal  
Also this paper uses the Directed Acyclic Graph Support Vector Machine (DAGSVM) for the multi-class electroencephalogram (EEG) signals classification.  ...  In this paper, we have proposed a new tree based wavelet transform (TBWT) for feature extraction scheme for epileptic seizure detection.  ...  ., 2001 for the benchmark EEG dataset available: (http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html).  ... 
doi:10.5121/sipij.2012.3111 fatcat:w532jwo5ijc53f4yfizk23ryii

Smoke detection in video using wavelets and support vector machines

Jayavardhana Gubbi, Slaven Marusic, Marimuthu Palaniswami
2009 Fire safety journal  
A novel method for smoke characterization using wavelets and support vector machines is proposed in this paper.  ...  The proposed algorithm is evaluated for its characterization properties using motion segmented images from a commercial surveillance system with good results.  ...  Rustom Kanga, iOmniscient Pty Ltd for data and feedback he provided during this work.  ... 
doi:10.1016/j.firesaf.2009.08.003 fatcat:kwq56l3asjfqlafjrlc5wjim6m

A Robust Polarmetric SAR Terrain Classification based on Sparse Deep Autoencoder Model Combined with Wavelet Kernel-based Classifier

Xiangdong Chen, Jianghong Deng
2020 IEEE Access  
INDEX TERMS Terrain classification, wavelet kernel, support vector machine, deep model, sparse coding, sigmoid function.  ...  Since the existing terrain classification algorithm based on deep learning is not ideal for unbalanced PolSAR classification, a effective terrain classification algorithm based on wavelet kernel sparse  ...  LEAST SQUARES SUPPORT VECTOR MACHINE Least squares support vector machine (SVM) is an improved model of support vector machine.  ... 
doi:10.1109/access.2020.2983478 fatcat:rekrb5xvvvedregnkufwfwvpiy

Intelligent Video Surveillance System based on Wavelet Transform and Support Vector Machine

Meghana M.Deshpande, Jaideep G. Rana
2012 International Journal of Computer Applications  
As human detection problem involves classification of two objects as humans and others, a human detection using intelligent video surveillance system is presented using support vector machine to detect  ...  In this paper, in order to improve the efficiency of the machine learning 2D Wavelet transform based features are used.  ...  Libin and Wenxin [7] presented a algorithm in gait identification based on Haar wavelet and support vector machine.  ... 
doi:10.5120/7419-0453 fatcat:jpr6tiuhdbchxby7vc6oiwvq4m

CAROTID PLAQUE CLASSIFICATION USING CONTOURLET FEATURES AND SUPPORT VECTOR MACHINES

Thangavel
2014 Journal of Computer Science  
The contourlet features with Support vector machine classifier produced classification accuracy of 85.6% compared to 81.3% accuracy in Adaboost classifier.  ...  The aim of this study is to propose a suitable and reliable system for better diagnosis and treatment of carotid diseases.  ...  Classification has been performed using Adaboost and Support Vector Machines classifiers.  ... 
doi:10.3844/jcssp.2014.1642.1649 fatcat:xjtdhln44facthkdgp6eigq6dm

Coarse-to-fine particle filters for multi-object human computer interaction

Matthias Ratsch, Clemens Blumer, Gerd Teschke, Thomas Vetter
2009 2009 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications  
In this paper we combine the Condensation and the Wavelet Approximated Reduced Vector Machine (W-RVM) approach.  ...  Efficient motion tracking of faces is an important aspect for Human Computer Interaction (HCI).  ...  Hudritsch's group at the Department of Computer Science, FHNW, Muttenz, in particular, P. Chappuis and D. Blanc for the cooperation at the 'Realtime Face Tracking' project, B.  ... 
doi:10.1109/idaacs.2009.5342945 fatcat:7ca5wuhoebe4zi3kc6k3txk6ny

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

Abdelouahad Achmamad, Atman Jbari
2020 Bulletin of Electrical Engineering and Informatics  
The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders.  ...  Keywords: Amyotrophic lateral sclerosis Classification Discrete wavelet transform Electromyography Support vector machine This is an open access article under the CC BY-SA license.  ...  Support vector machine SVM Support vector machine classifier is a type of supervised machine learning algorithm, it has a particular advantage in solving non-linear separation and high dimensional features  ... 
doi:10.11591/eei.v9i4.2381 fatcat:wcpejwmiivf5hah3cyceq76r4q
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