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Using Hadamard ECOC in multi-class problems based on SVM
2005
Interspeech 2005
unpublished
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In this paper, we propose to apply Hadamard Error-Correcting Output Code (Hadamard ECOC) to extend binary classifier for multi-class classification problems. ...
We combine it with binary support vector machine (SVM) to solve the multi-class problem of speaker identification, which takes advantage of error correcting ability of Hadamard ECOC and powerful classification ...
Acknowledgememts This work was supported in part by the National Nature Science Foundation of P.R.China under Grant NSFC 60372089. ...
doi:10.21437/interspeech.2005-672
fatcat:mfhnqaweqre4xl3whxim5votly
Study on fault diagnosis method for nuclear power plant based on hadamard error-correcting output code
2017
IOP Conference Series: Materials Science and Engineering
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NPP is a very complex system, so in fact the type of NPP failure may occur very much. ECOC is constructed by the Hadamard error correction code, and the decoding method is Hamming distance method. ...
The base models are established by lib-SVM algorithm. The result shows that this method can diagnose the faults of the NPP effectively. ...
In this paper, ECOC algorithm is used to realize multi-class SVM. ...
doi:10.1088/1757-899x/199/1/012035
fatcat:afqt73boxrcfpix3hbhh5qamfy
Research on Intrusion Detection Algorithm Based on Multi-Class SVM in Wireless Sensor Networks
2013
Communications and Network
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A multi-class method is proposed based on Error Correcting Output Codes algorithm in order to get better performance of attack recognition in Wireless Sensor Networks. ...
Aiming to enhance the accuracy of attack detection, the multi-class method is constructed with Hadamard matrix and two-class Support Vector Machines. ...
Conclusion In this paper, a multi-class SVM algorithm is constructed based on Hadamard coding algorithm. Through the experiment, higher accuracy of attack detecting is obtained. ...
doi:10.4236/cn.2013.53b2096
fatcat:f3s6xtmfpzdgxeir6dux45japi
Optimal N-ary ECOC Matrices for Ensemble Classification
[article]
2021
arXiv
pre-print
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Experimental results for six datasets demonstrate that using these deterministic coding matrices for N-ary ECOC classification yields comparable and in many cases higher accuracy compared to using randomly ...
This is particular true when N is adaptively chosen so that the dimension of M matches closely with the number of classes in a dataset, which reduces the loss in minimum Hamming distance when M is truncated ...
INTRODUCTION Error correcting output codes (ECOC) is an ensemble machine learning technique introduced by [1] for performing multi-class classfication based on Hamming distance. ...
arXiv:2110.02161v1
fatcat:7qrtqxxe4jh4vp3khjhnst2ivi
Multiclass Approaches for Support Vector Machine Based Land Cover Classification
[article]
2008
arXiv
pre-print
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Results from this study conclude the usefulness of One vs. One multi class approach in term of accuracy and computational cost over other multi class approaches. ...
One vs. one, one vs. rest, Directed Acyclic Graph (DAG), and Error Corrected Output Coding (ECOC) based multiclass approaches creates many binary classifiers and combines their results to determine the ...
Error-Correcting Output Code based approach The concept of Error-Correcting Output Coding (ECOC) based multi-class method is to apply binary (two-class) classifiers to solve the multi-class classification ...
arXiv:0802.2411v1
fatcat:fcffrqvmencafb7x6trzfdazvy
Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds
2021
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
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New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. ...
Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on classification accuracy. ...
[4] , and optimizing the learning of the base classifiers together as a multi-task learning problem [5] . ...
doi:10.1109/ictai52525.2021.00114
fatcat:6gja43cay5hpbhqifm6x5yv6nu
Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images
[article]
2016
arXiv
pre-print
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Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. ...
Moreover, it enables us to combine homogeneous datasets for applying advanced classification. ...
This work was supported in part by the National Natural Science Foundation of China (61422204 and 61473149), Jiangsu Natural Science Foundation for Distinguished Young Scholar (BK20130034) and NUAA Fundamental ...
arXiv:1609.00921v1
fatcat:oa6s2dt5bbcvrcyo6incwm4fvq
Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images
[article]
2016
bioRxiv
pre-print
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Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. ...
Moreover, it enables us to combine homogeneous datasets for applying advanced classification. ...
This work was supported in part by the National Natural Science Foundation of China (61422204 and 61473149), Jiangsu Natural Science Foundation for Distinguished Young Scholar (BK20130034) and NUAA Fundamental ...
doi:10.1101/092221
fatcat:megtap3u75bcrhfmb3ejder62i
An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
2019
Applied Sciences
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CNN and CNN-ECOC, and an alternative form called CNN-ECOC Regression, were evaluated in real-time implementation on a NAO humanoid robot. ...
We also propose and examine a combination model of CNN and a multi-binary classifier referred to as error correcting output code (ECOC) with the clean data. ...
Acknowledgments: The first author acknowledges the financial support of Umm Al-Qura University in Saudi Arabia that represented by the Saudi Arabian Cultural Bureau in Canada. ...
doi:10.3390/app9030470
fatcat:rpboxwuew5gh7hgy5rwdbrxmai
Pruning of Error Correcting Output Codes by optimization of accuracy–diversity trade off
2014
Machine Learning
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Error Correcting Output Code (ECOC) is one of the well-known ensemble techniques for multiclass classification which combines the outputs of binary base learners to predict the classes for multiclass data ...
In this paper, we propose a novel approach for pruning the ECOC matrix by utilizing accuracy and diversity information simultaneously. ...
In this study, KP is adapted to ECOC by using exactly the same logic. As with REP, SVM is used as base classifier for ECOC and CV is applied as in Sect. 3. ...
doi:10.1007/s10994-014-5477-5
fatcat:tdbuge7lknactaexfrnbx75doq
Monocular Vision-based Signer-Independent Pakistani Sign Language Recognition System using Supervised Learning
2016
Indian Journal of Science and Technology
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The proposed system was developed and the ten class supervised learning based system was able to achieve an accuracy of 83%. ...
method known as Support Vector Machine (SVM). ...
a ingle multi-class problem into multiple binary problems (total number of SVM models re given as: ) using data from two classes within each SVM model 32 . ...
doi:10.17485/ijst/2016/v9i25/96615
fatcat:xjvbsri2bvhvnjf7ln6mduluye
Large scale classification in deep neural network with Label Mapping
[article]
2018
arXiv
pre-print
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In recent years, deep neural network is widely used in machine learning. The multi-class classification problem is a class of important problem in machine learning. ...
Therefore, it is infeasible to solve the multi-class classification problem using deep neural network when the number of classes are huge. ...
Bakiri in [42] introduced ECOC to combine several binary classifiers to solve multi-class classification problems. ...
arXiv:1806.02507v1
fatcat:y5o5m7ii25fw7edp3pqvpvlgrm
Anatomical Pattern Analysis for decoding visual stimuli in human brains
[article]
2017
arXiv
pre-print
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Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. ...
Moreover, it enables us to combine homogeneous datasets for applying advanced classification. ...
This binary classification will be used in a one-versus-all ECOC method as a multiclass approach for classifying the categories of the brain response. ...
arXiv:1710.02113v1
fatcat:xtl7wksovbgxfc77xv3cgixndq
Kernel methods in Quantum Machine Learning
2019
Quantum Machine Intelligence
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In this paper, we review the latest developments regarding the usage of quantum computing for a particular class of machine learning algorithms known as kernel methods. ...
Quantum Machine Learning has established itself as one of the most promising applications of quantum computers and Noisy Intermediate Scale Quantum (NISQ) devices. ...
Also, in Windridge et al. (2018) , the authors propose a quantized version of Error Correction Output Codes (ECOC) which extends the QSVM algorithm to the multi-class case and enables it to perform an ...
doi:10.1007/s42484-019-00007-4
fatcat:hetltsur45drbayjhb2lpgv3di
Label-Embedding for Image Classification
2016
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. ...
The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. ...
ECOC approaches allow in particular to tackle multi-class learning problems as described by Dietterich and Bakiri in [14] . ...
doi:10.1109/tpami.2015.2487986
pmid:26452251
fatcat:233bczkvkvd45lzxyb7yygck6q
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