AI 용어집
인공지능 완전 사전
Intelligent Initialization
Optimization strategy that uses knowledge gained from previous optimizations to select promising starting points in the hyperparameter search space.
Meta-Optimizer
Algorithm designed to optimize the hyperparameters of other optimization algorithms, by learning the best search strategies adapted to different classes of problems.
Learning by Analogy
Meta-learning method that identifies structural similarities between tasks to efficiently transfer optimal hyperparameter configurations.
Conditional Search Space
Representation of the hyperparameter space where the valid values of certain hyperparameters conditionally depend on the values of other previously selected hyperparameters.
Sequential Model-Based Optimization
Bayesian optimization approach that builds a surrogate model of the objective function to efficiently guide the search for optimal hyperparameters.
Meta-Database
Structured set of previous optimization experiments containing hyperparameter configurations, achieved performance, and characteristics of the associated tasks.
Multi-Task Learning for Hyperparameters
Paradigm where hyperparameter optimization on multiple simultaneous tasks enables the discovery of robust configurations that are generalizable to new tasks.
Adaptive Warm-Starting
Initialization technique that dynamically selects optimal starting points based on the metric similarity between the current task and historical tasks.
Meta-Features
Quantitative and qualitative descriptors of a dataset or learning task that allow predicting optimal hyperparameters through metric similarity.
Contextual Bandit Optimization
Optimization approach that treats hyperparameter selection as a multi-armed bandit problem where context provides information about the current task.
Meta-Gradient Learning
Method that optimizes hyperparameters by computing gradients with respect to their performance on a set of meta-learning tasks, enabling fine adaptation.
Intelligent Hierarchical Search
Hyperparameter space exploration strategy that respects structural dependencies between parameters while leveraging meta-learning knowledge.
Hyperparameter Transfer
Process of migrating optimal hyperparameter configurations from a source domain to a target domain, with adaptation based on meta-features.
Reinforcement Learning for Hyperparameters
Formulation of hyperparameter optimization as a sequential decision problem where an agent learns an optimal hyperparameter selection policy.