A Prototype Procedure For Optimizing Training Strategies
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| Author | : Edward J. Matto |
| Publisher | : |
| Total Pages | : 66 |
| Release | : 1997 |
| Genre | : Military education |
| ISBN | : |
"For military units to be combat ready, they must be proficient in a collective set of tasks trained at various events. This report presents a research tool called the Training Strategies Optimization Prototype (TSOP) that shows potential to aid commanders in making decisions about how to improve strategies and schedules of training. TSOP was developed to determine whether available Army training data are adequate for systematically deriving alternative training strategies to meet commanders' needs. The prototype uses Army battalion level units, but is adaptable to scheduling problems at other echelons within the Army, for other services, and even for joint applications. This report illustrates TSOP's ability to provide the decision maker with an analytical means by which to schedule training events while considering both performance requirements and resource constraints. In many cases, the maximum level of troop proficiency may be attained through more than one combination of training events. TSOP is designed to allow the decision maker to identify the training strategy to attain and sustain troop proficiency within available resources."--DTIC.
| Author | : |
| Publisher | : |
| Total Pages | : 466 |
| Release | : 1991 |
| Genre | : Military research |
| ISBN | : |
| Author | : U.S. Army Research Institute for the Behavioral and Social Sciences |
| Publisher | : |
| Total Pages | : 54 |
| Release | : 1996 |
| Genre | : Military research |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : 248 |
| Release | : 2000 |
| Genre | : Social sciences |
| ISBN | : |
| Author | : |
| Publisher | : |
| Total Pages | : 56 |
| Release | : 1998 |
| Genre | : Social sciences |
| ISBN | : |
| Author | : Francisco J. Perales López |
| Publisher | : Springer |
| Total Pages | : 1170 |
| Release | : 2003-10-02 |
| Genre | : Computers |
| ISBN | : 3540448713 |
The refereed proceedings of the First Iberial Conference on Pattern Recognition and Image Analysis, IbPria 2003, held in Puerto de Andratx, Mallorca, Spain in June 2003. The 130 revised papers presented were carefully reviewed and selected from 185 full papers submitted. All current aspects of ongoing research in computer vision, image processing, pattern recognition, and speech recognition are addressed.
| Author | : Dr. Prakash Arumugam |
| Publisher | : Inkbound Publishers |
| Total Pages | : 291 |
| Release | : 2021-02-10 |
| Genre | : Computers |
| ISBN | : 8196822308 |
Embark on an extraordinary journey through the cutting-edge world of artificial intelligence with The Algorithmic Odyssey. This comprehensive guide serves as both a map and a compass for navigating the complex and rapidly evolving landscape of AI research. From the foundational principles of machine learning to the latest advancements in neural networks, this book offers a detailed exploration of the algorithms that are reshaping our world. Whether you are a seasoned researcher, a curious student, or a tech enthusiast, The Algorithmic Odyssey provides invaluable insights into the methodologies, challenges, and breakthroughs that define contemporary AI research. Discover the intricacies of supervised and unsupervised learning, delve into the depths of deep learning, and understand the transformative impact of reinforcement learning. Each chapter is meticulously crafted to offer clear explanations, practical examples, and thought-provoking discussions, making complex concepts accessible without sacrificing depth. Beyond the technicalities, The Algorithmic Odyssey also addresses the ethical, societal, and philosophical implications of AI. What does it mean to create intelligent systems? How do we ensure that these technologies benefit humanity? These questions and more are explored with rigor and sensitivity, encouraging readers to think critically about the future of AI. With contributions from leading experts in the field and a wealth of resources for further study, The Algorithmic Odyssey is an essential addition to the library of anyone passionate about the future of technology and its impact on our world. Join us on this odyssey and unlock the mysteries of artificial intelligence.
| Author | : Nikolaos F. Matsatsinis |
| Publisher | : Springer Nature |
| Total Pages | : 412 |
| Release | : 2020-01-21 |
| Genre | : Mathematics |
| ISBN | : 3030386295 |
This book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advancedresearch developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning.
| Author | : Oliver Kramer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 100 |
| Release | : 2013-12-04 |
| Genre | : Technology & Engineering |
| ISBN | : 3319034227 |
Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.
| Author | : Wenjie Zhang |
| Publisher | : Springer Nature |
| Total Pages | : 510 |
| Release | : |
| Genre | : |
| ISBN | : 981977232X |
