Core Concepts
Efficiently solve the CHF problem for weakly convex hypothesis classes over metric spaces using a general domain-independent algorithm.
Abstract
The article discusses learning weakly convex sets in metric spaces and proposes an algorithm to efficiently find consistent hypotheses. It explores the concept of weak convexity, its properties, and its application in machine learning. The algorithm iteratively computes representations of blockwise convex hulls of positive examples to solve the CHF problem.
Structure:
Introduction to Weak Convexity in Metric Spaces
Problem Statement: Consistent Hypothesis Finding (CHF)
Representation Schemes and Algorithm Design
Properties of Weakly Convex Sets and Blocks
Implementation Details and Algorithm Steps
The content provides insights into the theoretical foundations and practical applications of learning weakly convex sets in machine learning algorithms.