| 书目名称 | Support Vector Machines and Evolutionary Algorithms for Classification |
| 副标题 | Single or Together? |
| 编辑 | Catalin Stoean,Ruxandra Stoean |
| 视频video | |
| 概述 | Guides the reader from single methodologies, like support vector machines and evolutionary algorithms, to hybridization at different levels between the two, showing the benefits and drawbacks of each. |
| 丛书名称 | Intelligent Systems Reference Library |
| 图书封面 |  |
| 描述 | .When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this ‘masked hero’ be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations? Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance.. |
| 出版日期 | Book 2014 |
| 关键词 | Classification; Evolutionary Algorithms; Feature Selection; Machine Learning; Multimodal Optimization; Ru |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-06941-8 |
| isbn_softcover | 978-3-319-38243-2 |
| isbn_ebook | 978-3-319-06941-8Series ISSN 1868-4394 Series E-ISSN 1868-4408 |
| issn_series | 1868-4394 |
| copyright | Springer International Publishing Switzerland 2014 |
发表于 2025-3-21 17:50:54
