{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9798982","patent":{"patent_number":"US-9798982","title":"Determining a number of kernels using imbalanced training data sets","assignee":null,"inventors":[],"filing_date":"2015-08-20T00:00:00.000Z","publication_date":"2017-10-24T00:00:00.000Z","cpc_codes":["G06N","G06F","G06N"],"num_claims":12,"abstract":"Determining a number of kernels within a model is provided. A number of kernels that include data samples of a majority data class of an imbalanced training data set is determined based on a set of generated artificial data samples for a minority data class of the imbalanced training data set. The number of kernels within the model is generated based on the set of generated artificial data samples. A likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set is calculated. Parameters of each kernel in the number of kernels are updated based on the likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set. Each kernel in the number of kernels is adjusted based on the updated parameters."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Determining a number of kernels using imbalanced training data sets","description":"Determining a number of kernels within a model is provided. A number of kernels that include data samples of a majority data class of an imbalanced training data set is determined based on a set of ge","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9798982","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9798982","citation_suggestion":"Patentable. \"Determining a number of kernels using imbalanced training data sets\" (US-9798982). https://patentable.app/patents/US-9798982","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9798982","json":"https://patentable.app/api/llm-context/US-9798982","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T10:14:48.546Z"}