Function approximation is the process of estimating a target function using a simpler or more tractable model, often in scenarios where the exact function is unknown or too complex to compute directly. It is a fundamental technique in machine learning, enabling the creation of predictive models that generalize well from limited data.

Function Approximation

Function approximation

target function

Simpler model

tractable model

exact function

Complex Function

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.

machine learning

predictive models

Generalization is the cognitive process of applying learned information or skills from specific instances to broader contexts, allowing individuals and systems to make predictions and decisions in novel situations. It is crucial in both human learning and artificial intelligence, enabling the transfer of knowledge across different domains and reducing the need for extensive retraining.

Generalization

limited data

Using Chebyshev Polynomials for Effective Function Approximation

Function Approximation: The Versatility of Neural Networks

Polynomial Interpolation in the Complex Plane

Understanding Kernel Ridge Regression in Machine Learning

CUSTOMIZE YOUR LEARNING

CUSTOMIZE YOUR LEARNING

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