LEADER 03505nam 22005535 450 001 9911015870903321 005 20250702130327.0 010 $a981-9659-29-9 024 7 $a10.1007/978-981-96-5929-6 035 $a(MiAaPQ)EBC32189497 035 $a(Au-PeEL)EBL32189497 035 $a(CKB)39567936900041 035 $a(OCoLC)1526863288 035 $a(DE-He213)978-981-96-5929-6 035 $a(EXLCZ)9939567936900041 100 $a20250702d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDerivative-Free Optimization $eTheoretical Foundations, Algorithms, and Applications /$fby Yang Yu, Hong Qian, Yi-Qi Hu 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (288 pages) 225 1 $aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 311 08$a981-9659-28-0 327 $aIntroduction -- Preliminaries -- Framework -- Theoretical Foundation -- Basic Algorithm -- Optimization in Sequential Mode -- Optimization in High-Dimensional Search Space -- Optimization under Noise -- Optimization with Parallel Computing. 330 $aThis book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book?s structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material. 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aMachine learning 606 $aArtificial intelligence 606 $aMachine Learning 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aArtificial Intelligence. 676 $a006.31 700 $aYu$b Yang$0799781 701 $aQian$b Hong$0505899 701 $aHu$b Yi-Qi$01833476 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015870903321 996 $aDerivative-Free Optimization$94408372 997 $aUNINA