The ultimate goal of machines is to help humans to solve problems. Such problems range between two extremes: structured problems for which the solution is totally defined (and thus are easily programmed by humans), and random problems for which the solution is completely undefined (and thus cannot be programmed). Problems in the vast middle ground have solutions that cannot be well defined and are, thus, inherently hard to program. Machine Learning is the way to handle this vast middle ground, so that many tedious and difficult hand-coding tasks would be replaced by automatic learning methods. There are several machine learning tasks, and this work is focused on a major one, which is known as classification. Some classification problems are hard to solve, but we show that they can be decomposed into much simpler sub-problems. We also show that independently solving these sub-problems by taking into account their particular demands, often leads to improved classification performance.
Marshall's study on the Epistles of John constitute a single volume in The New International Commentary on the New Testament. Prepared by some of the world's leading scholars, the series provides an exposition of the New Testament books that is thorough and fully abreast of modern scholarship yet faithful to the Scriptures as the infallible…
How to download book
Buy this book
You can buy this book now only for $33.89. This is the lowest price for this book.
Download book free
If you want to download this book for free, please register, approve your account and get one book for free.
After that you may download book «Demand-Driven Associative Classification»: