{"product_id":"unsupervised-learning","title":"Unsupervised Learning","description":"\u003cp\u003e\u003cb\u003eA new approach to unsupervised learning\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEvolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge—for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers.\u003c\/p\u003e \u003cp\u003eInspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, \u003ci\u003eUnsupervised Learning: A Dynamic Approach\u003c\/i\u003e presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data—from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.\u003c\/p\u003e \u003cp\u003eSelf-organization concepts and applications discussed include:\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eDistance metrics for unsupervised clustering\u003c\/li\u003e\n\u003cli\u003eSynaptic self-amplification and competition\u003c\/li\u003e\n\u003cli\u003eImage retrieval\u003c\/li\u003e\n\u003cli\u003eImpulse noise removal\u003c\/li\u003e\n\u003cli\u003eMicrobiological image analysis\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eUnsupervised Learning: A Dynamic Approach\u003c\/i\u003e introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":54222508425560,"sku":"9780470278338","price":122.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9780470278338_22ee1b8c-0e7e-4ee5-b57b-4b50b81d091c.jpg?v=1780023682","url":"https:\/\/agendabookshop.com\/products\/unsupervised-learning","provider":"Agenda Bookshop","version":"1.0","type":"link"}