scholarly journals Identifiability of covariance kernels in the Gaussian process regression model

2021 ◽  
Vol 32 (6) ◽  
pp. 1373-1392
Author(s):  
Jae Hoan Kim ◽  
Jaeyong Lee
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Gaussian Process Regression

Anomaly Detection in Video Surveillance via Gaussian Process

2015 ◽  
Vol 29 (06) ◽  
pp. 1555011 ◽  
Author(s):  
Nannan Li ◽  
Xinyu Wu ◽  
Huiwen Guo ◽  
Dan Xu ◽  
Yongsheng Ou ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Regression Model ◽  
Gaussian Process ◽  
Video Surveillance ◽  
Gaussian Process Regression ◽  
Bayesian Regression ◽  
Current Frame ◽  
Motion Patterns ◽  
Online Clustering ◽  
Efficient Calculation

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.

Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir

2019 ◽  
Vol 33 (11) ◽  
pp. 3929-3947 ◽  
Author(s):  
Masood Akbari ◽  
Farzin Salmasi ◽  
Hadi Arvanaghi ◽  
Masoud Karbasi ◽  
Davood Farsadizadeh
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Discharge Coefficient ◽  
Gaussian Process Regression ◽  
Piano Key Weir

Validation of Adaptive Gaussian Process Regression Model Used for SIF Prediction

2018 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Maximum Absolute Error ◽  
Data Points

The main aim of this paper is to perform the validation of the adaptive Gaussian process regression model (AGPRM) developed by the authors for the Stress Intensity Factor (SIF) prediction of a crack propagating in topside piping. For validation purposes, the values of SIF obtained from experiments available in the literature are used. Sixty-six data points (consisting of L, a, c and SIF values obtained by experiments) are used to train the AGPRM, while four independent data sets are used for validation purposes. The experimental validation of the AGPRM also consists of the comparison of the prediction accuracy of AGPRM and Finite Element Method (FEM) relative to the experimentally derived SIF values. Four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (MAE), and Coefficient of Determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of the AGPRM is comparable with and even higher than that of the FEM, provided the training points of the AGPRM are aptly chosen.

scholarly journals Searching for rewards in graph-structured spaces

2019 ◽  
Author(s):  
Eric Schulz ◽  
Charley M Wu
Keyword(s):  
Reinforcement Learning ◽  
Regression Model ◽  
Gaussian Process ◽  
Predictive Model ◽  
Human Behavior ◽  
Model Comparison ◽  
Gaussian Process Regression ◽  
Diffusion Kernel ◽  
Function Learning ◽  
Psychological Models

How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization.

scholarly journals Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1428 ◽  
Author(s):  
Katalin Blix ◽  
Károly Pálffy ◽  
Viktor Tóth ◽  
Torbjørn Eltoft
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Aquatic Environments ◽  
Lake Balaton ◽  
Spectral Bands

The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.

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