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New $γ$-VC Dimension Parameter Analyzed for Weak Learner Boosting

By PulseAugur Editorial · [1 sources] ·

This paper introduces and analyzes the $γ$-VC dimension, a parameter that characterizes the sample complexity for converting weak hypotheses into accurate predictors. The research refines the relationship between the traditional VC dimension and this new parameter. Additionally, it provides updated upper and lower bounds for the $γ$-VC dimension concerning decision stumps and axis-parallel rectangles in $\mathbb{R}^d$. AI

IMPACT Introduces a new parameter for analyzing the expressive power of weak learners, potentially improving understanding of boosting algorithms.

RANK_REASON The item is a research paper detailing theoretical concepts in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New $γ$-VC Dimension Parameter Analyzed for Weak Learner Boosting

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The item is a research paper detailing theoretical concepts in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Boosting and the Expressive Power of Simple Weak Learners via the $γ$-VC Dimension

    Boosting converts weak hypotheses with a small edge over random guessing into highly accurate predictors, but the expressive power of the resulting classifier can depend strongly on the structure of the base class. We study this phenomenon through the $γ$-VC dimension introduced …

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