Metaheuristics in Machine Learning: Theory and Applications

Metaheuristics in Machine Learning: Theo

📁 Metaheuristics in Machine Learning: Theory and Applications (Studies in Computational Intelligence, 967)

✍ Diego Oliva (editor), Essam H. Houssein (editor), Salvador Hinojosa (editor) 📂 Library 📅 2021 🏛 Springer 🌐 English

<div><div>This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are them

Metaheuristics for Machine Learning - Al

📁 Metaheuristics for Machine Learning - Algorithms and Applications

✍ Kanak Kalita; Narayanan Ganesh; S. Balamurugan 📂 Library 📅 2024 🏛 WILEY 🌐 English

The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex op

Hybrid Metaheuristics in Structural Engi

📁 Hybrid Metaheuristics in Structural Engineering: Including Machine Learning Applications

✍ Gebrail Bekda, Sinan Melih Nigdeli 📂 Library 📅 2023 🏛 Springer 🌐 English

From the start of life, people used their brains to make something better in design in ordinary works. Due to that, metaheuristics are essential to living things, and several inspirations from life have been used in the generation of new algorithms. These algorithms have unique features, but the usa

Hybrid Metaheuristics in Structural Engi

📁 Hybrid Metaheuristics in Structural Engineering: Including Machine Learning Applications

✍ Gebrail Bekdaş; Sinan Melih Nigdeli 📂 Library 📅 2023 🏛 Springer Nature 🌐 English

From the start of life, people used their brains to make something better in design in ordinary works. Due to that, metaheuristics are essential to living things, and several inspirations from life have been used in the generation of new algorithms. These algorithms have unique features, but the usa

Innovations in Machine Learning: Theory

📁 Innovations in Machine Learning: Theory and Applications

✍ David Heckerman, Christopher Meek, Gregory Cooper (auth.), Professor Dawn E. Hol 📂 Library 📅 2006 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

<p><P>Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning,

Innovations in Machine Learning: Theory

📁 Innovations in Machine Learning: Theory and Applications

✍ David Heckerman, Christopher Meek, Gregory Cooper (auth.), Professor Dawn E. Hol 📂 Library 📅 2006 🏛 Springer 🌐 English