Work

Improved Noise-Tolerant Learning and Generalized Statistical Queries

Year: 1994

Type: report

Abstract: The statistical query learning model can be viewed as a tool for creating (or demonstrating the existence of) noise-tolerant learning algorithms in the PAC model. The complexity of a statistical query algorithm, in conjunction with the complexity of simulating SQ algorithms in the PAC model with noise, determine the complexity of the noise-tolerant PAC algorithms produced. Although roughly optimal upper bounds have been shown for the complexity of statistical query learning, the corresponding noisetolerant PAC algorithms are not optimal due to inefficient simulations. In this paper we provide both improved simulations and a new variant of the statistical query model in order to overcome these inefficiencies. We improve the time complexity of the classification noise simulation of statistical query algorithms. Our new simulation has a roughly optimal dependence on the noise rate. We also derive a simpler proof that statistical queries can be simulated in the presence of classification n...

Source: Digital Access to Scholarship at Harvard (DASH) (Harvard University)

Authors: Javed A. Aslam, Scott E. Decatur

Institution: Harvard University

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Harvard University, Defense Advanced Research Projects Agency, National Defense Science and Engineering Graduate

OpenAlex ID: W2333814403

Cites: 16

Cited by: 7

Related to: 10