伊夫·博纳富瓦（1923—2016），20世纪法国最重要的诗人、翻译家、文学评论家，也是“二战”后世界诗坛举足轻重的巨匠，很多人将其与波德莱尔、马拉美和瓦雷里放在同等重要的位置上。1953年出版《杜弗的动与静》，博纳富瓦一举成名。他既是技艺的大师，又是独特的沉思者。他的诗既透明又神秘，飞扬而深刻，是法国超现实主义诗歌的进一步发展和对超现实主义的超越，也是现代诗歌中的奇峰。

There are,of course,some gastronomic treats to be had on the way,including a feast at a converted petrol station and a rendezvous with the famous Marseillaise Bouillabaisse.But when lunch is over there are still more moments to relish:learning the secrets of perfume-blendin at a school for noses,the story of a crime passionnel perpetrated by a jealous husband on an oversexed,cy...(展开全部)

There are,of course,some gastronomic treats to be had on the way,including a feast at a converted petrol station and a rendezvous with the famous Marseillaise Bouillabaisse.But when lunch is over there are still more moments to relish:learning the secrets of perfume-blendin at a school for noses,the story of a crime passionnel perpetrated by a jealous husband on an oversexed,cy...(展开全部)

安娜·加瓦尔达，法国当红女作家，1970年生于巴黎，29岁时她的第一部短篇小说集《我希望有人在什么地方等我》（中译本已由漓江出版社出版）创下了100多万册的销售佳绩，使她从一个平凡的中学教师一跃成为法国光芒四射的明星作家，再次把灰姑娘变公主的童话变成了现实。出书前她在文学界没有任何关系，也不在巴黎生活，却凭借一部短篇小说集征服了千千万万的法国读者。

安娜·加瓦尔达，法国当红女作家，1970年生于巴黎，29岁时她的第一部短篇小说集《我希望有人在什么地方等我》（中译本已由漓江出版社出版）创下了100多万册的销售佳绩，使她从一个平凡的中学教师一跃成为法国光芒四射的明星作家，再次把灰姑娘变公主的童话变成了现实。出书前她在文学界没有任何关系，也不在巴黎生活，却凭借一部短篇小说集征服了千千万万的法国读者。

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and th...(展开全部)