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Mean field variational Bayesian inference for support vector machine classification [article]

Jan Luts, John T. Ormerod
2013 arXiv   pre-print
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented.  ...  Acknowledgments This research was partially supported by Australian Research Council Discovery Project DP110100061.  ...  Introduction Support vector machines (SVMs) and its variants remain one of the most popular classification methods in machine learning and has been successfully utilized in many applications.  ... 
arXiv:1305.2667v1 fatcat:i2v4w7n5kbgzdmn5bsnv75ppsa

Conditional Random Fields and Support Vector Machines: A Hybrid Approach [article]

Qinfeng Shi, Mark D. Reid, Tiberio Caetano
2010 arXiv   pre-print
We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector  ...  Machines (SVMs).  ...  Introduction Conditional Random Fields (CRFs) and Support Vector Machines (SVMs) can be seen as representative of two different approaches to classification problems.  ... 
arXiv:1009.3346v1 fatcat:jsw6q7vyqfdoxa64dp6tlihele

Mean field method for the support vector machine regression

J.B. Gao, S.R. Gunn, C.J. Harris
2003 Neurocomputing  
First, we will show how support vector machine (SVM) regression problem can be solved as the maximum a posteriori prediction in the Bayesian framework.  ...  Partial support was also provided by the Natural Science Foundation of China (Grant No.: 19871032). The ÿrst author wishes to thank Ole Winther and Tommi S.  ...  It is well known that support vector machines (SVM) can be interpreted as the maximum a posteriori (MAP) prediction with a Gaussian prior, i.e., GP, under the Bayesian framework so that some statistical  ... 
doi:10.1016/s0925-2312(02)00573-8 fatcat:7hks45w6zvh3ncewshzutrluvu

Mean field variational Bayesian inference for support vector machine classification

Jan Luts, John T. Ormerod
2014 Computational Statistics & Data Analysis  
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented.  ...  Acknowledgments This research was partially supported by Australian Research Council Discovery Project DP110100061.  ...  Introduction Support vector machines (SVMs) and its variants remain one of the most popular classification methods in machine learning and has been successfully utilized in many applications.  ... 
doi:10.1016/j.csda.2013.10.030 fatcat:pesfytazbjhb5ezaycafedgy74

Image Modeling Using Gibbs-Markov Random Field And Support Vector Machines Algorithm

Refaat M Mohamed, Ayman El-Baz, Aly A. Farag
2007 Zenodo  
This paper introduces a novel approach to estimate the clique potentials of Gibbs Markov random field (GMRF) models using the Support Vector Machines (SVM) algorithm and the Mean Field (MF) theory.  ...  This formulation of the GMRF model urges the use of the SVM with the Mean Field theory applied for its learning for estimating the energy function.  ...  SUPPORT VECTOR MACHINES REGRESSION In this paper the Support Vector Machines (SVM) number of Occurance of each clique shape Fig. 4 4 The estimated mixture of Gaussians distributions using MF-based  ... 
doi:10.5281/zenodo.1073448 fatcat:6dbxnksybff5rpdg3imvyknxmm

A Heterogeneous FPGA Architecture for Support Vector Machine Training

Markos Papadonikolakis, Christos-Savvas Bouganis
2010 2010 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines  
Support Vector Machines is a powerful supervised learning tool. Its training phase, however, is a time-consuming task and heavily dependent on the training dataset size and dimensionality.  ...  INTRODUCTION Support Vector Machines (SVMs) [1] are a popular supervised learning method for classification and regression problems.  ...  This is the first work in the field of accelerating SVM training through an FPGA device that makes full use of the custom number presentation supported by the device and aims at full utilization of its  ... 
doi:10.1109/fccm.2010.39 dblp:conf/fccm/PapadonikolakisB10 fatcat:46iqyid72bfcdbg6my3nyxtwjy

Support vector machine applications in the field of hydrology: A review

Sujay Raghavendra. N, Paresh Chandra Deka
2014 Applied Soft Computing  
In the recent few decades there has been very significant developments in the theoretical understanding of Support vector machines (SVMs) as well as algorithmic strategies for implementing them, and applications  ...  This paper reviews the state-of-the-art and focuses over a wide range of applications of SVMs in the field of hydrology.  ...  programming support vector machines. • Nu-support vector machines.  ... 
doi:10.1016/j.asoc.2014.02.002 fatcat:gkcq34t4mnhibcotphbrnbusba

Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines [chapter]

Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bistritz, Jöerg Sander, Russell Greiner
2005 Lecture Notes in Computer Science  
In this paper, we investigate the tumor segmentation performance of a recent variant of DRF models that takes advantage of the powerful Support Vector Machine (SVM) classification method.  ...  Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation.  ...  Greiner is supported by NSERC and the Alberta Ingenuity Centre for Machine Learning (AICML). C.-H. Lee is supported by NSERC, AICML, and iCORE.  ... 
doi:10.1007/11569541_47 fatcat:zywdyrjjzjawppann3pawsmyya

Errata: Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery

Shanshan Li
2011 Journal of Applied Remote Sensing  
This paper presents an improved method for integrating a support vector machine (SVM) and Markov random field to classify the hyperspectral imagery.  ...  Terms of Use: http://spiedl.org/terms Li et al.: Adaptive support vector machine and Markov random field model... Shanshan Li received his BS degree from Wuhan University, Wuhan, China, in 2004.  ...  Terms of Use: http://spiedl.org/terms Li et al.: Adaptive support vector machine and Markov random field model...  ... 
doi:10.1117/1.3628662 fatcat:jgsawqs7hfe2bk4ov7r4fihba4

POS Tagging in Amazighe Using Support Vector Machines and Conditional Random Fields [chapter]

Mohamed Outahajala, Yassine Benajiba, Paolo Rosso, Lahbib Zenkouar
2011 Lecture Notes in Computer Science  
We have used state-of-art supervised machine learning approaches to build our POS-tagging models.  ...  In our approach we use sequence classification techniques based on two state-of-art machine learning approaches, namely: Support Vector Machines (SVMs) and Conditional Random Fields (CRFs), to build our  ...  Later on, machine learning based POS-tagging proved to be both less laborious and more effective than the rule based ones.  ... 
doi:10.1007/978-3-642-22327-3_28 fatcat:qazruempt5hinlgyaddkjhd7gu

A Reconfigurable Multiclass Support Vector Machine Architecture for Real-Time Embedded Systems Classification

Jason Kane, Robert Hernandez, Qing Yang
2015 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines  
SVM Introduction • Support Vector Machines (Binary Classification) -Hyperplanes constructed based on Largest Margin between data of one class and another. • Points lying on this margin are termed "Support  ...  SVM Introduction • Support Vector Machines (Binary Classification SVM) -Classification technique using machine supervised learning • 1995 Vapnik & Cortes [1] -SVM Models loaded prior to test  ... 
doi:10.1109/fccm.2015.24 dblp:conf/fccm/KaneHY15 fatcat:6pr3oo6vljchfmm5vcdjwb2mku

Estimation of spectro-temporal receptive fields based on linear support vector machine classification

Arne F Meyer, Max FK Happel, Frank W Ohl, Jörn Anemüller
2009 BMC Neuroscience  
We show that the STRF model is equivalent to the structure of a linear support vector machine (SVM) and propose the use of SVMs for the estimation of the STRF.  ...  Introduction The spectro-temporal receptive field (STRF) of a neuron is defined as the linear filter that, when convolved with the spectro-temporal representation of an arbitrary stimulus, gives a linear  ... 
doi:10.1186/1471-2202-10-s1-p147 fatcat:sganovzj2za2jdczvceuxykgwm

ESTIMASI KEDALAMAN PADA CITRA DENGAN CONDITIONAL RANDOM FIELD (CRF) DAN STRUCTURED SUPPORT VECTOR MACHINE (SSVM) MENGGUNAKAN FITUR FFT

Derry Alamsyah, Muhammad Rachmadi
2018 Jurnal TAM  
Untuk menyelesaikan permasalahan tersebut, penelitian ini menggunakan Conditional Random Field dan Structured Support Vector Machine (CRF-SSVM) sebagai metode untuk estimasi.  ...  Model CRF: (7) Normalisasi Faktor: Structured Support Vector Machine (SSVM) SSVM merupakan bentuk terstruktur dari metode SVM.  ... 
doaj:4afa430e7e80408486854e1d4c0a3732 fatcat:y67ywtj6zrdddiyumhtm7cyw3i

Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine

Yajun Chen, Zhangnan Wu, Bo Zhao, Caixia Fan, Shuwei Shi
2020 Sensors  
On the basis of k-mean clustering image segmentation using color information and connected region analysis, a method combining multi feature fusion and support vector machine (SVM) was proposed to identify  ...  Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field.  ...  Ma and other researchers used the histogram of oriented gradient (HOG) feature and support vector machine (SVM) to identify grape leaves, which are robust to light and environmental changes, but they could  ... 
doi:10.3390/s21010212 pmid:33396255 fatcat:hezop3sek5a6rnh2gkqh7h6gke

Riemann Kernel Support Vector Machine Recursive Feature Elimination in the Field of Compound Limb Motor Imagery BCI

Xuewen TAO
2019 Journal of Mechanical Engineering  
为提升复合肢体动作想象相关脑电信号特征的特异性并降低不同通道间的 信息混淆,提出了一种基于脑电流形特征信息刻划的黎曼核支持向量机递归特征筛选方法(Riemann kernel support vector machine recursive feature elimination, RKSVM-RFE)。  ...  To solve this problem, a new method named Riemann kernel support vector machine recursive feature elimination (RKSVM-RFE) is proposed based on the manifold information on electroencephalogram (EEG).  ...  7 种类别则需要计算得到 7 个一对多滤波器, 最后的特征矢量 f 可表示为 ir i ir i= = ⎛ ⎞ ⎜ ⎟ = ⎜ ⎟ ⎝ ⎠ ∑ f f f f f f f f W X f W X ( Support vector machine, SVM)的核函数,提取七 种复合动作的流形特征,并结合 SVM 的回归特征 筛选法(Recursive feature elimination, RFE)  ... 
doi:10.3901/jme.2019.11.131 fatcat:6u6vtaxupvcynpge25rruxxoym
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