weixin_38201392的博客

It’s hard to compare intrepid explorers like Ferdinand Magellan, James Cook, and Roald Amundsen with someone, well, like me. While these adventurers braved the elements, wild nature, and unknown dangers to discover new worlds (at least for their civilization), my biggest physical achievement to date would probably be completing a 10-kilometer charity quarter-marathon—walking. The explorers of old had it good, of course, when it came to choices of unexplored places to stake their claim on. Christopher Columbus only had to sail due west from Europe, and he discovered two entire continents. For us, there are far fewer choices. There isn’t much landmass on Earth that is yet unexplored; even the Mariana Trench, the deepest part of the world’s oceans, has been conquered. But explorer I am, and explorer you will be in this book. While much of the known physical world has been conquered (see Figure P-1), the unknown still looms over most of us. We are all born with a sense of wonder and amazement at the world around us. Many of us just learn to turn it off as we grow older and jaded. I believe this is partly because we don’t understand what goes on in the world around us well enough, and thus we don’t care either. Click the remote and the TV turns on—why and how does that work? The first time we tried to ask, we were probably given a blank stare or waved away—who cares as long as you can watch the next season of American Idol? That soon grows to be our reaction as well. Preface

The theory and practice of time series analysis have developed rapidly since the appear- ance in 1970 of the seminal work of George E. P. Box and Gwilym M. Jenkins, Time Series Analysis: Forecasting and Control, now available in its third edition (1994) with co-author Gregory C. Reinsel. Many books on time series have appeared since then, but some of them give too little practical application, while others give too little theoretical background. This book attempts to present both application, and theory at a level acces- sible to a wide variety of students and practitioners. Our approach is to mix application and theory throughout the book as they are naturally needed.

MongoDB is a document-oriented database management system designed for performance, horizontal scalability, high availability, and advanced queryability. See the following wiki pages for more information about MongoDB: • Introduction • Philosophy • About If you want to download MongoDB, see the downloads page. If you’d like to learn how to use MongoDB with your programming language of choice, see the introduction to the drivers (page 393).

We design and experiment with an innovative way to automatically generate product profiles from Amazon reviews. Using NLP, we extract opinions from each review, clusters them by their orientation through an unsupervised learning (k-means). From these clustered opinions, we estimate the product profiling kernel θ and the pricing kernel λ. Finally, we optimize/update the word polarity by minimizing the prediction error (supervised learning). While the trained model perform only slightly better than random guessing, the interim outputs and the estimated parameters seems to provide useful information while showing a possibility for improvement.

We design and experiment with an innovative way to automatically generate product profiles from Amazon reviews. Using NLP, we extract opinions from each review, clusters them by their orientation through an unsupervised learning (k-means). From these clustered opinions, we estimate the product profiling kernel θ and the pricing kernel λ. Finally, we optimize/update the word polarity by minimizing the prediction error (supervised learning). While the trained model perform only slightly better than random guessing, the interim outputs and the estimated parameters seems to provide useful information while showing a possibility for improvement.