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Efficient Approaches to Gaussian Process Classification

Lehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther
1999 Neural Information Processing Systems  
We present three simple approximations for the calculation of the posterior mean in Gaussian Process classification.  ...  The first two methods are related to mean field ideas known in Statistical Physics.  ...  Acknowledgements: BS would like to thank the Leverhulme Trust for their support (F /250/K). The work was also supported by EPSRC Grant GR/L52093.  ... 
dblp:conf/nips/CsatoFOSW99 fatcat:j4aaoc3brnamhpxqbhokbw7a6y

Image Texture Classification using Fuzzy Inclusion and Fuzzy Entropy Measures

M.Subba Rao
2020 International Journal of Advanced Trends in Computer Science and Engineering  
They are not versatile to accommodate human thinking and thus the evolving demands and desires of real-life processes. We only make a choice between including and excluding a feature.  ...  Selection of features plys a crucial role in enhancing Machine Learning efficiency as it significantly improves the performance of Texture classification by discarding insignificant features from the original  ...  RESULTS AND DISCUSSIONS A systematic performance evaluation is done to determine the efficiency of the texture classification of the proposed process using the current Global Process and the Local Method  ... 
doi:10.30534/ijatcse/2020/103942020 fatcat:mo2d632wlna6doqtygi64hidyi

Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN

Srimathi J, Valli Mayil
2019 International Journal of Computer Networks & Communications  
In the sink node, Reweight Boosting Classification is carried out to classify the sensed DP and it sends to the base station (BS) for further processing.  ...  In order to improve the energy efficient data gathering in WSN, a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced with lesser time consumption.  ...  The approach measures the relationship among sensing information of the SNs, it failed to use efficient classification method to classify collected data.  ... 
doi:10.5121/ijcnc.2019.11207 fatcat:i6cwcjdnxza47pa2bpjn3soaha

Efficient Gaussian process classification using random decision forests

B. Fröhlich, E. Rodner, M. Kemmler, J. Denzler
2011 Pattern Recognition and Image Analysis  
Vol. 21 No. 2 2011 EFFICIENT GAUSSIAN PROCESS CLASSIFICATION 187  ...  DOI: 10.1134/S1054661811020337 Efficient Gaussian Process Classification The two main modelling assumptions for Gaussian process classifiers are as follows: 1.  ... 
doi:10.1134/s1054661811020337 fatcat:xwlmahntrvgzrnikph4hsurcqq

Guest editorial: selected papers from ICIMCS 2013

Meng Wang, Ke Lu, Gang Hua, Cees Snoek
2014 Multimedia Systems  
In the second paper, "Large-margin Multiview Gaussian Process", Xu et al. introduce a large-margin Gaussian process approach for discovering discriminative latent subspace shared by multiple features.  ...  This approach is demonstrated to outperform conventional feature coding methods.  ...  Finally, we express our thanks to all the authors who have contributed to the special issue.  ... 
doi:10.1007/s00530-014-0428-3 fatcat:vpxj3voqnff4bp6n5nptslc2m4

Scalable Inference for Gaussian Process Models with Black-Box Likelihoods

Amir Dezfouli, Edwin V. Bonilla
2015 Neural Information Processing Systems  
Our approach maintains the statistical efficiency property of the original AVI method, requiring only expectations over univariate Gaussian distributions to approximate the posterior with a mixture of  ...  Experiments on small datasets for various problems including regression, classification, Log Gaussian Cox processes, and warped GPs show that our method can perform as well as the full method under high  ...  A sensible modeling approach to the above problem is to assume that the Q latent functions {f j } are uncorrelated a priori and that they are drawn from Q zero-mean Gaussian processes [25] : p(f ) = Q  ... 
dblp:conf/nips/DezfouliB15 fatcat:fhx5627irzdm7imeppau36lcry

A Classification Method Using Data Reduction

Daiho Uhm, Sung-Hae Jun, Seung-Joo Lee
2012 International Journal of Fuzzy Logic and Intelligent Systems  
Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation.  ...  In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification.  ...  Therefore, this research contributed an efficient approach to the classification task.  ... 
doi:10.5391/ijfis.2012.12.1.1 fatcat:owkjec4h6fbqpheokb2folgupm

GPU-Accelerated Gaussian Processes for Object Detection

Calum Blair, John Thompson, Neil Robertson
2015 2015 Sensor Signal Processing for Defence (SSPD)  
Gaussian Process classification (GPC) allows accurate and reliable detection of objects.  ...  We describe our version of accelerated GPC on GPU (Graphics Processing Unit). GPUs have limited memory so any GPC implementation must be memory-efficient as well as computationally efficient.  ...  Gaussian Process Classification Here we concentrate on binary classification of samples with labels y ∈ {0, 1}.  ... 
doi:10.1109/sspd.2015.7288505 fatcat:x43fpulddfax7ez73m6tdozurm

Hierarchical Facial Age Estimation Using Gaussian Process Regression

Manisha M. Sawant, Kishor Bhurchandi
2019 IEEE Access  
Compared with existing single level Gaussian process approaches for age estimation, our approach is computationally efficient at both the levels of hierarchy.  ...  It consists of multi-class Gaussian process classifier to classify the input images into different age groups followed by a warped Gaussian process regression to model group specific aging patterns.  ...  Computationally efficient extension of WGP has been proposed in [7] using Orthogonal Gaussian Process (OGP).  ... 
doi:10.1109/access.2018.2889873 fatcat:dmjp7g2adbc3bjhfc6knjtl2de

Comparison of Machine Learning Algorithms for Induction MotorRotor SingleFault Diagnosis using Stator Current Signal

2021 International Journal of Advanced Trends in Computer Science and Engineering  
This quantitive researchwas conducted to compare the efficiency between three groups of machine learning' classification techniques for detecting broken rotor bar (BRB)fault in induction motor using stator  ...  Hence, the study found there are five classifiers (Fine Gaussian SVM, Fine KNN, Weighted KNN, Bagged Trees and Subspace KNN) are best suited for the proposed problem when providing nearly 100% classification  ...  Among the classification functions, the functions give the best efficiency are Fine Gaussian SVM, Fine KNN and Weighted KNN with 100% efficient.In the future, multiple-faults diagnogis problem will be  ... 
doi:10.30534/ijatcse/2021/031032021 fatcat:5wej3ikonrhkrartzf7k4bu4ci

Bayesian regression and classification using mixtures of Gaussian processes

J.Q. Shi, R. Murray-Smith, D.M. Titterington
2003 International Journal of Adaptive Control and Signal Processing  
For a large data-set with groups of repeated measurements, a mixture model of Gaussian process priors is proposed for modelling the heterogeneity among the different replications.  ...  The classification model is illustrated on a synthetic example.  ...  Acknowledgements The authors gratefully acknowledge support from EPSRC grant Modern statistical approaches to off-equilibrium modelling for nonlinear system control GR/M76379/01.  ... 
doi:10.1002/acs.744 fatcat:6ik4mrphxbfdrcrlnsiv7iuogq

Robust Classification and Semi-supervised Object Localization with Gaussian Processes [chapter]

Alexander Lütz
2011 Lecture Notes in Computer Science  
We formulate the problem as a kernel hyperparameter optimization task and utilize the Gaussian process framework.  ...  To perform the computations efficiently we present techniques reducing the necessary time effort from cubically to quadratically for essential parts of the computations.  ...  In Sect. 2 we will briefly review classification with Gaussian processes, present our approach for object localization with hyperparameter optimization and show techniques for efficient computations.  ... 
doi:10.1007/978-3-642-23123-0_48 fatcat:xk773okj7nccjhkx6g4wyr4qyu

One-Shot Learning of Object Categories Using Dependent Gaussian Processes [chapter]

Erik Rodner, Joachim Denzler
2010 Lecture Notes in Computer Science  
We present how to use dependent Gaussian processes for transferring knowledge from a related category in a non-parametric Bayesian way.  ...  Our method is able to select this category automatically using efficient model selection techniques.  ...  Classification with Gaussian Process Priors In the following we will briefly review Gaussian process regression and classification.  ... 
doi:10.1007/978-3-642-15986-2_24 fatcat:2vl4ebiw2ffbhb7x6tio7hzeue


2021 Journal of Engineering & Technological Advances  
Efficient segmentation performed by Morphological processing and Otsu's threshold.  ...  Using support vector machine (SVM) classifier to classify tumours (Neuroblastoma) and the results compare with the prevailing-nearest neighbour (KNN) model.  ...  Efficient neuron segmentation is proposed using SVM and KNN classification approaches for segmenting neuronal structure.  ... 
doi:10.35934/segi.v6i2.9 fatcat:et2f4b6m2vclpgb3pwoifibszq

Real-Time Moving Object Classification with Automatic Scene Division

Zhaoxiang Zhang, Yinghao Cai, Kaiqi Huang, Tieniu Tan
2007 2007 IEEE International Conference on Image Processing  
With efficient features extracted and organized, the approach can be real-time and achieve high classification accuracy.  ...  We address the problem of moving object classification. Our aim is to classify moving objects of traffic scene videos into pedestrians, bicycles and vehicles.  ...  With the subregion strategy and Gaussian Assumption, 2D features are efficiently organized and the approach achieves good performance.  ... 
doi:10.1109/icip.2007.4379787 dblp:conf/icip/ZhangCHT07 fatcat:xtn2dr4tcjf3pettgfgy4kwflu
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