Luc Devroye's A Probabilistic Theory of Pattern Recognition (Stochastic PDF

By Luc Devroye

ISBN-10: 0387946187

ISBN-13: 9780387946184

A self-contained and coherent account of probabilistic suggestions, masking: distance measures, kernel principles, nearest neighbour principles, Vapnik-Chervonenkis conception, parametric class, and have extraction. every one bankruptcy concludes with difficulties and routines to extra the readers knowing. either learn staff and graduate scholars will make the most of this wide-ranging and up to date account of a quick- relocating box.

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Extra resources for A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)

Example text

For example, a linear model makes the assumption that the data is fundamentally linear and that the distance between the instances and the straight line is just noise, which can safely be ignored. For example, for simple tasks you may evaluate linear models with various levels of regularization, and for a complex problem you may evaluate various neural networks. In the next chapters we will dive deeper and write more code, but before we do, make sure you know how to answer the following questions: 1.

It is sometimes called the Manhattan norm because it measures the distance between two points in a city if you can only travel along orthogonal city blocks.

Before you read on, pause and try to answer these questions for yourself. Finally, there is no continuous flow of data coming in the system, there is no particular need to adjust to changing data rapidly, and the data is small enough to fit in memory, so plain batch learning should do just fine. TIP If the data was huge, you could either split your batch learning work across multiple servers (using the MapReduce technique, as we will see later), or you could use an online learning technique instead.

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A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) by Luc Devroye

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