Gaussian Process (binary) Classification
Adversarial vulnerability bounds for Gaussian process classification
... use Gaussian process classification (GPC), a powerful and widely adopted classifier, as the basis for our analysis and demonstrate the AB method provides meaningful bounds for a variety of datasets, ...
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Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
... Gaussian process classification Hensman and Matthews (2015) consider Gaussian process classification with a pro- bit inverse link function and suggest a variational ...
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Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting
... on Gaussian Process Classification for House-Hold objects Recognition & Sorting Aamir Khan 1 , Li Sun 2 , Gerardo Aragon-Camarasa 1 , ...on Gaussian Process (GP) ...
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Sparse Gaussian Process Classification With Multiple Classes
... optimized software for the binary case can be used directly. However, the binary partitions (“output codings”) and the posthoc combination scheme have to be chosen in a heuris- tic and rather arbitrary way. Even if a ...
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Assessing Approximate Inference for Binary Gaussian Process Classification
... In this section we describe the Gaussian process model for binary classification (GPC). Let y ∈ {− 1,1 } denote the class label corresponding to an input x. The GPC model is discriminative in the ...
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Mind the nuisance: Gaussian process classification using privileged noise
... The results of these experiments are shown in Figure 2 in terms of pairwise comparisons over 45 binary tasks between GPC+ and the main baselines, GPC and SVM+. The complete results with the error of each method GPC, ...
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Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood
... GP classification, determining the moments of the tilted distribution requires solv- ing only one-dimensional integrals, and assuming the probit likelihood function, these univariate integrals can be computed ...
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Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI
... Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. Researchers and clinicians are also increasingly interested in the study of ...
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Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling
... The problem we consider poses substantial statistical and computational challenges. The main statistical framework for inference with binary responses and latent variables are GP classifiers, which are mathematically ...
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PAC-Bayesian Generalization Error Bounds for Gaussian Process Classification
... SV classification such as Sequential Minimal Optimisation (SMO) proposed by Platt (1998) can often tackle problems with rather large sample sizes n in much less than O(n 3 ) (average case), by concentrating ...
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Active Online Anomaly Detection using Dirichlet Process Mixture Model and Gaussian Process Classification
... Dirichlet process mixture model (DPMM), a non- parametric approach to learn mixture models that also infers the number of clusters in a data-driven ...object classification [11], scene classi- fication [4, ...
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Gaussian process optimization with failures: classification and convergence proof
... revisited Gaussian process classification with a model based on observation ...sharp classification boundaries, which were appropriate in our framework, and allowed us to propose the first ...
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Discriminative Gaussian Process Latent Variable Model for Classification
... accurate classification may be possible if the data lie on a low-dimensional ...sian Process Latent Variable Models can discover low dimensional manifolds given only a small number of examples, but learn a ...
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Gaussian process convolutions for Bayesian spatial classification
... Because the calculations were fast, it was reasonable to run the MCMC chain for a large number of iterations and then thin it heavily. It would be useful to find a more efficient sampler so that fewer samples are ...
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pyGPs -- A Python Library for Gaussian Process Regression and Classification
... pyGPs is a Python software project implementing Gaussian processes ( gp s) for machine learning ( ml ). gp s have become a popular model for a wide variety of ml tasks (Rasmussen and Williams, 2006), such as ...
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Scalable Hierarchical Gaussian Process Models for Regression and Pattern Classification
... sparse Gaussian processes for regression,” Machine Learning, ...2018. Gaussian process models have become the dominant approach to nonparamet- ric Bayesian regression, but their limitation is the ...
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Parsimonious Gaussian Process Models for the Classification of Multivariate Remote Sensing Images
... a Gaussian mixture model in a kernel fea- ture space by assuming that the data of each class live in a specific ...of classification accuracy, some of the proposed models perform equivalently to a SVM but ...
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Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images
... Index Terms—Kernel methods, remote sensing images, parsi- monious Gaussian process, hyperspectral, classification. I. I NTRODUCTION Thanks to the development of different Earth observation missions, ...
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Parsimonious Gaussian process models for the classification of hyperspectral remote sensing images
... parsimonious Gaussian process models is reviewed and 5 additional models are proposed to provide more flexibility to the classifier in the context of hyperspectral image ...a Gaussian mixture model ...
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A Review on Genre Classification and Emotion Estimation of Audio Signals using Gaussian Process
... piece. Human categorization of music appears natural, yet it can be inconsistent, changing, and, in some cases may even seem arbitrary. Though human reactions or judgments are influenced by the audio signal, but also ...
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