Scalable Pattern Recognition Algorithms: Applications in Computational Biology and Bioinformatics

This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

本書旨在提供一個統一的框架，描述軟計算和機器學習技術如何在建立高效的模式識別模型中被明智地制定和使用。本書回顧了已確立和尖端的研究，提供了理論、算法和應用的謹慎平衡，特別強調在計算生物學和生物信息學中的應用。特點包括：將不同的軟計算和機器學習方法與模式識別任務相結合；詳細討論了不同技術在決策中處理不確定性和高效挖掘大型生物數據集的整合；特別強調實際應用，如微陣列表達數據集和磁共振圖像；包含大量例子和實驗結果來支持所描述的理論概念；每章結束時提供未來研究方向和全面的參考文獻。