2009/06/02 by Juan‐Manuel Torres‐Moreno, Juan-Manuel Torres-Moreno, Torres-Moreno, Juan-Manuel +2
Computer Science · Earth and Planetary Sciences · #Blind Source Separation Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Underwater Acoustics Research #cs.LG
paper · pdf · doi:10.48550/arxiv.0906.0470
8 pages, 6 tables
arxiv created 2009/06/02 · openalex publication_date 2009/06/02 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The problem of classifying sonar signals from rocks and mines first studied by Gorman and Sejnowski has become a benchmark against which many learning algorithms have been tested. We show that both the training set and the test set of this benchmark are linearly separable, although with different hyperplanes. Moreover, the complete set of learning and test patterns together, is also linearly separable. We give the weights that separate these sets, which may be used to compare results found by other algorithms.