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Glosarium AI

Kamus lengkap Kecerdasan Buatan

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Gaussian Process

A non-parametric probabilistic model that defines a distribution over functions, characterized by a mean function and a covariance function (kernel).

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Acquisition Function

A heuristic function that guides the selection of the next evaluation point by balancing exploration and exploitation based on the surrogate model.

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Expected Improvement (EI)

An acquisition function that calculates the expected improvement over the current best observation, weighted by its probability of occurrence.

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Probability of Improvement (PI)

An acquisition function that maximizes the probability of improvement over the current best point, without considering the magnitude of the improvement.

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Kernel Function

A function that measures the similarity between input points and defines the covariance structure of the Gaussian process, influencing the smoothness of the modeled function.

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Hyperprior

A prior distribution on the model's hyperparameters (such as kernel parameters), allowing for the automatic learning of these parameters through Bayesian inference.

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Sequential Model-Based Optimization (SMBO)

A general optimization framework that uses a sequential model to propose new evaluation points based on previous observations.

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Multi-Objective Bayesian Optimization

An extension of Bayesian Optimization to handle multiple conflicting objectives, searching for the optimal Pareto front rather than a single optimum.

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Noise Handling

Techniques for modeling and managing noise in objective function observations, essential for real-world applications with noisy evaluations.

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Parallel Bayesian Optimization

Variant that proposes several points simultaneously for parallel evaluation, using acquisition criteria adapted for batch selection.

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Kernel Design

Process of designing or selecting the appropriate covariance function to capture the specific properties of the objective function.

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Acquisition Optimization

Secondary optimization problem consisting of maximizing the acquisition function to find the next candidate evaluation point.

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