{"product_id":"9783032408136","title":"Learning in Intelligent Models under Explainability and Sample Constraints Escaping Low-Data Regimes via Explainability for Medical and Fault Diagnosis Systems","description":"\u003ch3\u003eSpringerBriefs in Computer Science\u003c\/h3\u003e\u003ch1\u003eLearning in Intelligent Models under Explainability and Sample Constraints\u003c\/h1\u003e\u003ch2\u003eEscaping Low-Data Regimes via Explainability for Medical and Fault Diagnosis Systems\u003c\/h2\u003e\u003ch3\u003eDarian M. Onchis\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Business \u0026amp; Productivity Software \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\u003cp\u003eThis book provides a unified framework for learning in intelligent systems under conditions of limited data and strict explainability requirements. It addresses a critical gap in modern machine learning, where high-performance models often rely on large datasets and operate as black boxes, limiting their applicability in high-stakes domains.The book introduces the LIMESC framework, a novel approach that integrates explainability, learning, and domain knowledge into a single methodological structure. It systematically explores how models can remain robust, interpretable, and adaptable when data are scarce, noisy, or evolving.Core topics include neural computing, deep learning under small-data regimes, regularization and optimization strategies, class-incremental learning without memory, and dataset knowledge transfer. The book further examines post-hoc explainability methods and transitions toward intrinsic interpretability through causal and neuro-symbolic approaches. Additional perspectives such as topological data analysis and reinforcement learning in constrained environments are also presented.The framework is grounded in real-world applications, particularly medical diagnostics and fault detection systems, where explainability and reliability are essential. Through a combination of theoretical insights and practical methodologies, the book offers a structured pathway toward designing adaptive and interpretable machine learning systems.This book is intended for researchers, advanced graduate students, and practitioners in machine learning, artificial intelligence, and biomedical engineering.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cp\u003eDarian M. Onchis is a researcher in machine learning, explainable artificial intelligence (XAI), and neuro-symbolic systems, with a particular focus on learning under data constraints. His work bridges statistical learning, deep learning, and structured knowledge integration, with applications in medical diagnostics and fault detection systems.\u003c\/p\u003e\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e20 December 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032408136\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e130\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":53983015796876,"sku":"9783032408136","price":179.99,"currency_code":"USD","in_stock":true}],"url":"https:\/\/lateknightbooks.com\/products\/9783032408136","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}