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• W3164467453 abstract "The foundational assumption of machine learning is that the data under consideration is separable into classes; while intuitively reasonable, separability constraints have proven remarkably difficult to formulate mathematically. We believe this problem is rooted in the mismatch between existing statistical techniques and commonly encountered data; object representations are typically high dimensional but statistical techniques tend to treat high dimensions a degenerate case. To address this problem, we develop a dedicated statistical framework for machine learning in high dimensions. The framework derives from the observation that object relations form a natural hierarchy; this leads us to model objects as instances of a high dimensional, hierarchal generative processes. Using a distance based statistical technique, also developed in this paper, we show that in such generative processes, instances of each process in the hierarchy, are almost-always encapsulated by a distinctive-shell that excludes almost-all other instances. The result is shell theory, a statistical machine learning framework in which separability constraints (distinctive-shells) are formally derived from the assumed generative process." @default.
• W3164467453 created "2021-06-07" @default.
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• W3164467453 date "2021-01-01" @default.
• W3164467453 modified "2023-09-23" @default.
• W3164467453 title "Shell Theory: A Statistical Model of Reality" @default.
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• W3164467453 doi "https://doi.org/10.1109/tpami.2021.3084598" @default.
• W3164467453 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34048335" @default.
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