Finite Hypothesis Class/finite Hypothesis Analysis
- Python for Integrated Circuits - - An Online Book - |
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Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In machine learning, a finite hypothesis class refers to a set of candidate hypotheses or models that can be used to approximate a target function. This class is finite, meaning it contains a finite number of hypotheses. You can represent a finite hypothesis class using mathematical notation as follows: Let H = {h_1, h_2, ..., h_K} In this notation:
Mathematically, you can express the concept of a finite hypothesis class using an inequality as follows: |H| <= K ----------------------------------------- [3992] Where:
This inequality states that the size of the hypothesis class (the number of possible models) is less than or equal to K, meaning there are at most K hypotheses in the class. In practice, a finite hypothesis class is often used in settings like binary classification, where there is a limited set of possible models or decision boundaries that can be considered for solving a particular problem. This finite hypothesis class can make learning more tractable and help prevent overfitting in some cases. ============================================
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