It's a kind of problem I come across way too often: in trying to determine whether a specific condition exists, a number of symptoms might help the diagnosis. But, some of the symptoms can also appear when something else is going on, instead. On the other hand, not all of the symptoms necessarily appear when the condition in fact is active. The question then becomes, given a number of indicators that have some diagnostic value, and given that all of them are inaccurate some of the time, how do I combine the indicators' answers to get the best diagnosis of that condition?
This book proposes "Boosting" as the answer. Start with remarkably few technical requirements on the diagnostic indicators, plus some number of cases in which the condition's presence or absence is already known. Given that, Boosting iteratively determines the weight to assign each indicator. One requirement is that each indicator suggests presence or absence of the condition at least somewhat differently than random guessing - and being wrong most of the time is just as useful as being right more often than not, since the algorithm automatically assigns negative weights to such indicators.
After that, the authors present rigorous development in a number of directions. I emphasize "rigor" - this text offers detailed development, analysis, and formal proof of the algorithm and its properties, far beyond the needs of someone who just wants to implement the technique. Implementable detail is there, but you'll spend a fair bit of time teasing it out of the dense notation used here.
Then, once basics have been established, the discussion branches out. The authors offer game-theoretic analysis of the algorithm, along with comparisons to related optimization techniques. After tightening some of the technical requirements, they apply it to decision trees with binary outcomes, to N-ary classification problems for N>2, and to more complex kinds of tasks, always with the same mathematical rigor.
Frankly, it's a bit much for someone just looking to get the broad picture or someone looking to code something up fast and try it out. But, it's not meant for those readers. To get the most out of it, you should come prepared to take your time, work out the meaning of each equation, follow the development carefully, and maybe even do some of the exercises. If you've already plowed through some "yellow books," you'll know what I mean, even though this doesn't come from the Springer yellow series. For the prepared and patient reader, this has my highest recommendation
-- wiredweird
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