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Everything about Learnable function class
In statistical learning theory, a learnable function class is a set of functions for which an algorithm can be devised to asymptotically minimize the expected risk, uniformly over all probability distributions. The concept of learnable classes are closely related to regularization in machine learning, and provides large sample justifications for certain learning algorithms. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Learnable function classA 5-minute orientation
- Skim a recent paper: Gaussian-type activation function with learnable parameters in complex-valued convolutional neural network and its application for PolSAR classificationInfluential work since 2022
- Pick up a classic book: Second language acquisition and task-based language teachingThe most-reprinted book on the subject
- Read the landmark paper: Balanced Learnable Activation Function (BLAF): Enhancing Classification Accuracy in Deep Neural NetworksCited 0 times
- Go sideways: Empirical risk minimizationA closely related topic
- Go sideways: Statistical learning theoryA closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Balanced Learnable Activation Function (BLAF): Enhancing Classification Accuracy in Deep Neural Networks
NeuroPlast: a learnable activation function evaluated under knowledge distillation for medical image classification
🆕 Influential since 2022
đź“š Books worth reading
Second language acquisition and task-based language teachingMichael H. Long · 2015
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
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