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A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment

Yunchen Kong, Xue Ma, Chenglin Wen
2022 Sensors  
First, a support vector machine expert labeling system is constructed by using a bag-of-words model to extract image features from a small number of labeled samples.  ...  The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge annotation, is preliminarily solved in this  ...  , which are used to train a shallow model support vector machine.  ... 
doi:10.3390/s22030898 pmid:35161644 pmcid:PMC8839952 fatcat:nvxoynpd6vajhm5og7ecz76tgu

Optimization of the SVM Kernels Using an Empirical Error Minimization Scheme [chapter]

Nedjem-Eddine Ayat, Mohamed Cheriet, Ching Y. Suen
2002 Lecture Notes in Computer Science  
However, our optimization scheme minimizes an empirical error estimate using a Quasi-Newton technique. The method has shown to reduce the number of support vectors along the optimization process.  ...  We address the problem of optimizing kernel parameters in Support Vector Machine modelling, especially when the number of parameters is greater than one as in polynomial kernels and KMOD, our newly introduced  ...  The classification duration is , thus, greatly decreased. Hand-written digits recognition Support Vector Machine is a binary classifier which is useful for two-class data only.  ... 
doi:10.1007/3-540-45665-1_28 fatcat:ohrmy5mnljb7tmj3yicf46qqfy

A Novel Approach for Efficient SVM Classification with Histogram Intersection Kernel

Gaurav Sharma, Frederic Jurie
2013 Procedings of the British Machine Vision Conference 2013  
However, at test time, it requires evaluating the kernel with each one of the support vectors, which is time consuming.  ...  The kernel trick -commonly used in machine learning and computer vision -enables learning of non-linear decision functions without having to explicitly map the original data to a high dimensional space  ...  Aknowledgements This research was supported by a grant from the Conseil Régional de Basse-Normandie (CRBN/11P01269/REPERE).  ... 
doi:10.5244/c.27.10 dblp:conf/bmvc/SharmaJ13 fatcat:xzpddiqixvdy5h7ydt6wdxic3q

Polyhedral Conic Classifiers for Visual Object Detection and Classification

Hakan Cevikalp, Bill Triggs
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
The methods have properties and run-time complexities comparable to linear Support Vector Machines (SVMs), and they can be trained from either binary or positive-only samples using constrained quadratic  ...  Our experiments show that they significantly outperform both linear SVMs and existing one-class discriminants on a wide range of object detection, open set recognition and conventional closed-set classification  ...  For both reasons it is useful for the discriminant to focus on tightly bounding the positive class whereas conventional discriminants such as Support Vector Machines (SVMs) treat the two classes as though  ... 
doi:10.1109/cvpr.2017.438 dblp:conf/cvpr/CevikalpT17 fatcat:tx2fgihhcjgm5nr4ewiydf5jua

Latent Support Measure Machines for Bag-of-Words Data Classification

Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada
2014 Neural Information Processing Systems  
Support vector machines (SVMs) are widely used tools for such classification problems.  ...  Then the latent SMM finds a separating hyperplane that maximizes the margins between distributions of different classes while estimating latent vectors for words to improve the classification performance  ...  This work was supported by JSPS Grant-in-Aid for JSPS Fellows (259867).  ... 
dblp:conf/nips/YoshikawaIS14 fatcat:vntwwro6tvcszi6sojnr5zvmci

Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization [article]

Rishabh Iyer, John T. Halloran, Kai Wei
2018 arXiv   pre-print
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization.  ...  .), and a family of machine learning classifiers and regressors (Logistic Regression, SVMs, Least Square Regression, etc.).  ...  Acknowledgments This work was partially supported by NIH NCATS grant UL1 TR001860.  ... 
arXiv:1807.06574v1 fatcat:q3y3cacac5ayvneimkzupwm5h4

{'en_US': 'Mathematical and Quantitative Methods'}

Collective Authors
2021 Acta Universitatis Danubius: Oeconomica  
I used the Matlab programming language to implement linear and nonlinear classificators and apply this on the dataset.  ...  Support Vector Machines (SVMs)have found many applications in various fields. They have been introduced for classification problems and extended to regression.  ...  The best classification for linearly inseparable case, were obtained for polynomial and radial basis kernels which underlines once again the importance of a correct choice for the kernel function used.  ... 
doaj:8e724b2f768b4e3c99d1ab269d08fc06 fatcat:4a6dsholhfdl5pfgp664tkgaea

Study of SVM based Indian Stock Market Prediction Methods

Nitin Rameshrao Talhar, Awesha Tomar, Tapasya Ghorpade, Namrata Sakore, Mayuri Zile
2019 Zenodo  
In this survey paper, we present an analysis of the various works done in the field of support vector machines for the prediction of stock market returns.  ...  The various variables, dataset and their impact on the accuracy of the prediction are explained for each model. Thus helping investors to select their preferred model for prediction.  ...  LIMITATIONS OF EXISTING MODEL Every model that uses support vector machines suffers from the problem of over-fitting, this problem has been han-dled by using quasi-linear support vector machines.  ... 
doi:10.5281/zenodo.2624191 fatcat:cuua2hwl3fgq3gii5a32nyfuly

Study of SVM based Indian Stock Market Prediction Methods

Nitin Rameshrao Talhar, Awesha Tomar, Tapasya Ghorpade, Namrata Sakore, Mayuri Zile
2019 Zenodo  
In this survey paper, we present an analysis of the various works done in the field of support vector machines for the prediction of stock market returns.  ...  The various variables, dataset and their impact on the accuracy of the prediction are explained for each model. Thus helping investors to select their preferred model for prediction.  ...  LIMITATIONS OF EXISTING MODEL Every model that uses support vector machines suffers from the problem of over-fitting, this problem has been han-dled by using quasi-linear support vector machines.  ... 
doi:10.5281/zenodo.2624190 fatcat:ptn56vi3mnbj3khqp7tjrp2hcu

Study of SVM based Indian Stock Market Prediction Methods

Nitin Rameshrao Talhar, Awesha Tomar, Tapasya Ghorpade, Namrata Sakore, Mayuri Zile
2019 Zenodo  
In this survey paper, we present an analysis of the various works done in the field of support vector machines for the prediction of stock market returns.  ...  The various variables, dataset and their impact on the accuracy of the prediction are explained for each model. Thus helping investors to select their preferred model for prediction.  ...  LIMITATIONS OF EXISTING MODEL Every model that uses support vector machines suffers from the problem of over-fitting, this problem has been han-dled by using quasi-linear support vector machines.  ... 
doi:10.5281/zenodo.2624394 fatcat:d4xbr6ym7jbu5nt2jz2xcjrh6y

Deconstructing Kernel Machines [chapter]

Mohsen Ali, Muhammad Rushdi, Jeffrey Ho
2014 Lecture Notes in Computer Science  
In particular, for polynomial kernels, additional O(m 3 ) queries are sufficient to reconstruct the entire decision boundary, providing a set of quasi-support vectors that can be used to efficiently evaluate  ...  Specifically, we assume the feature space R d is known and the kernel machine has m support vectors such that d > m (or d >> m), and in addition, the classifier C is laconic in the sense that for a feature  ...  The kernel machine defined by the quasi-support vectors should be a good approximation of the original kernel machine and this is shown in Table 1a , where we compare the classification results using  ... 
doi:10.1007/978-3-662-44848-9_3 fatcat:xkwquzzff5amdaerndfjycecsq

A Hierarchical Approach in Tamil Phoneme Classification using Support Vector Machine

S. Karpagavalli, E. Chandra
2015 Indian Journal of Science and Technology  
In each hierarchical level, different number of models is built using Support Vector Machine (SVM) for classifying each phoneme group/phoneme.  ...  Most of the speech recognition systems are designed based on the sub-word unit phoneme which is the basic sound unit of a language.  ...  Support Vector Machine has been used to build the different number of models in each stage.  ... 
doi:10.17485/ijst/2015/v8i35/80681 fatcat:supeajt35vdxpmiiojcre5yqi4

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Jakob Thrane, Jesper Wass, Molly Piels, Julio C. M. Diniz, Rasmus Jones, Darko Zibar
2017 Journal of Lightwave Technology  
Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio (OSNR) estimation and modulation  ...  format classification, respectively.  ...  During training, we pass several 8-D input vectors of features, paired up with their corresponding target class, here the modulation format, to a linear support vector machine classifier.  ... 
doi:10.1109/jlt.2016.2590989 fatcat:2lkn6jpijbcmnhlt7iphk2vt3e

Fast and simple gradient-based optimization for semi-supervised support vector machines

Fabian Gieseke, Antti Airola, Tapio Pahikkala, Oliver Kramer
2014 Neurocomputing  
A prominent research direction in the field of machine learning are semi-supervised support vector machines.  ...  The resulting method can be implemented easily using black-box optimization engines and yields excellent classification and runtime results on both sparse and non-sparse data sets.  ...  The authors would like to thank the anonymous reviewers for valuable comments and suggestions on an early version of this work.  ... 
doi:10.1016/j.neucom.2012.12.056 fatcat:j4lxkbgemrel7gfqwqnsjk2dpq

Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Makhamisa Senekane
2019 Machine Learning and Knowledge Extraction  
In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP.  ...  SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%.  ...  Acknowledgments: The author acknowledges the support of National University of Lesotho Research and Innovations Committee. Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/make1010029 fatcat:o4nvytzyzvh2ppajsyit5e5waa
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