[PDF] Top 20 Gaussian Processes for Machine Learning (GPML) Toolbox
Has 10000 "Gaussian Processes for Machine Learning (GPML) Toolbox" found on our website. Below are the top 20 most common "Gaussian Processes for Machine Learning (GPML) Toolbox".
Gaussian Processes for Machine Learning (GPML) Toolbox
... For Gaussian likelihoods, in- ference is analytically tractable; however, in many tasks, Gaussian likelihoods are not appropriate, and approximate inference methods such as Expectation Propagation (EP) ... See full document
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Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for prediction of skin temperature in lower limb prostheses
... Many amputees complain of increased temperature and perspi- ration within their prosthetic socket [1, 2] . The most accurate tem- perature reading would be obtained by placing the sensor directly in contact with the ... See full document
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A practical design and implementation of a low cost platform for remote monitoring of lower limb health of amputees in the developing world
... However, the financial costs associated with it are sub- stantially high as around 75% of those affected by diabetes live in middle or low income countries [2]. Many healthcare technologies and products presume that ... See full document
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Efficient modeling of latent information in supervised learning using Gaussian processes
... Machine learning has been very successful in providing tools for learning a function mapping from an input to an output, which is typically referred to as supervised ...and machine translation ... See full document
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Active learning of intuitive control knobs for synthesizers using gaussian processes
... the machine reduce the dimensionality of the problem by select- ing a subset of parameters, as illustrated by labels 1 and 2 in Figure ...the machine to learn the concept and apply it to a starting sound in ... See full document
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DPPy: DPP Sampling with Python
... point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statis- tics, and more recently machine ... See full document
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Learning Kernels over Strings using Gaussian Processes
... Mart´ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Cor- rado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael ... See full document
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Semi-described and semi-supervised learning with Gaussian processes
... GP learning involved the cluster assumption [Lawrence and Jordan, 2005] for clas- ...In machine learning, Ghahra- mani and Jordan [1994] learn the joint density of the in- put and output data and ... See full document
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GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
... for Gaussian process regression (Chen and Ren, 2009, ...general machine learning problems although DDM is only applicable for spatial data ... See full document
5
Detecting periodicities with Gaussian processes
... and machine learning (Rasmussen & Williams, ...and machine learning framework, the solution of the interpolation problem corresponds to the expectation of a Gaussian process, Z , ... See full document
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Deep Gaussian Processes
... Boltzmann machine (BM) at the core of one of the most interesting approaches to modern ma- chine learning is very much a case of a the field going back to the future: BMs rose to prominence in the early ... See full document
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Digital Communication Receivers Using Gaussian Processes for Machine Learning
... most machine learning textbooks introduce nonlinear regression [21, 32, 35] and it helps understanding GPR as a nonlinear MMSE ...for Gaussian likelihood ... See full document
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ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel
... Other toolboxes support the ability to distribute workloads across multiple computers. For ex- ample, a client machine executing the Weka Experimenter module can distribute its workload via Java Remote Method ... See full document
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Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
... Online Learning of Gaussian Processes (COOL-GP) framework for enabling a massive number of GP inference agents to simultaneously perform (a) efficient online updates of their GP models using their ... See full document
8
On the usage of active learning for SHM
... active learning method seeks to find data which contains the most informative points and as result to identify these data points that will give the trained classifier the best performance in terms of reduced ... See full document
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Sparse Online Gaussian Processes
... è ¸ådëçî |îæçå øJæ ì¸ë'øJùdî iëçè¾îõd÷!äxéõiä1ø_ä¨ådæçèXé !øçùdî å ãÃðëçî ¸ëçîæçæçèXì¸éuïíeì¸éDøçè¾éOådì¸ådæ èXédîvñ¡údøJùdîcêdëçìxê9ì¸æÄîeõ æçêdäxëçæÄîãÃð ä ¸ìxëçèXøçùd÷ 'èXøçùä ,îe[r] ... See full document
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String and Membrane Gaussian Processes
... The comparison between the spectral mixture kernel and the string spectral mixture kernel is of particular interest, since spectral mixture kernels are pointwise dense in the family of stationary kernels, and thus can be ... See full document
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Gaussian Processes for Ordinal Regression
... Gaussian processes (O’Hagan, 1978; Neal, 1997) have provided a promising non-parametric Bayesian approach to metric regression (Williams and Rasmussen, 1996) and classification prob- lems (Williams and ... See full document
23
Gaussian Process Modelling for Uncertainty Quantification in Convectively-Enhanced Dissolution Processes in Porous Media
... A GP can be interpreted as a family of random variables, any finite number of which have a joint Gaussian distribution. A GP is fully specified by its mean function and covariance function [45]. A GP emulator is a ... See full document
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Deep Learning and Machine Learning in Hydrological Processes, Climate Change and Earth Systems: A Systematic Review
... In this section, the machine learning methods have been classified into the following popular subsections, i.e., tree-based, support vector-based, neural net- work-based, and hybrids and ensembles. Further, ... See full document
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