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Improving GP-UCB Algorithm by Harnessing Decomposed Feedback
Gaussian processes (GPs) have been widely applied to machine learning and nonparametric approximation. Given existing observations, a GP allows the...
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An Empirical Study of Progressive Insular Cooperative GP
Genetic programming (GP) is a general purpose artificial intelligence method, that breeds populations of computer programs to solve a given problem,...
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Progressive Insular Cooperative GP
This work presents a novel genetic programming system for multi-class classification, called progressively insular cooperative genetic...
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Bayesian projection pursuit regression
In projection pursuit regression (PPR), a univariate response variable is approximated by the sum of M “ridge functions,” which are flexible...
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QR decomposition based low rank approximation for Gaussian process regression
This paper presents a QR decomposition based low-rank approximation algorithm for training and prediction in Gaussian process regression....
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Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set
In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from...
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Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and the understanding of their...
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Multi-region symbolic regression: combining functions under a multi-objective approach
This paper introduces Multi-Region Symbolic Regression (MR-SR), a general framework that divides the original input data space of symbolic regression...
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A multistep forecasting method for online car-hailing demand based on wavelet decomposition and deep Gaussian process regression
The main objective of this paper is to develop a high-precision forecasting method that can forecast the probability distribution of the demand value...
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Structure-Preserving Gaussian Process Dynamics
Most physical processes possess structural properties such as constant energies, volumes, and other invariants over time. When learning models of...
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On the impact of prior distributions on efficiency of sparse Gaussian process regression
Gaussian process regression (GPR) is a kernel-based learning model, which unfortunately suffers from computational intractability for irregular...
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Bayesian Decision Theory and Expected Improvement
The previous chapter used Gaussian processes (GP) as the surrogate model to approximate the underlying objective function. GP is a flexible framework...
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Regression
In regression, the objective is to predict the value of a target Y ϵ R given a feature vector X ϵ Rd, using sample data and/or information about the...
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Incorporating Actor-Critic in Monte Carlo tree search for symbolic regression
Most traditional genetic programming methods that handle symbolic regression are random algorithms without memory and direction. They repeatedly...
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Future Flight Safety Monitoring: Comparison of Different Computational Methods for Predicting Pilot Performance Under Time Series During Descent by Flight Data and Eye-Tracking Data
Introduction. Effective and real-time analysis of pilot performance is important for improving flight safety and enabling remote flight safety...
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How to Turn Your Camera into a Perfect Pinhole Model
Camera calibration is a first and fundamental step in various computer vision applications. Despite being an active field of research, Zhang’s method...
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EVARS-GPR: EVent-Triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data
Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external...
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Towards Explainable AutoML Using Error Decomposition
The important process of choosing between algorithms and their many module choices is difficult, even for experts. Automated machine learning allows...
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A novel adaptive artifacts wavelet Denoising for EEG artifacts removal using deep learning with Meta-heuristic approach
Electroencephalogram (EEG) is said to be a common tool to control neurological disorders, performed medical diagnoses, and cognitive research. But,...
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MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning
Evolutionary ensemble learning methods with Genetic Programming have achieved remarkable results on regression and classification tasks by employing...