Basic Compression Library
by Marcus Geelnard
Release 1.2.0
2006-07-22
introductIOn
The Basic Compression Library is a library of well known compression algorithms implemented in portable ANSI C code.
For more information about the Basic Compression Library, please read the manual (doc/manual.pdf) and, of course, the source code.
Learning Kernel Classifiers: Theory and Algorithms, introductIOn This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introductIOn of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
The purpose of this paper is to provide a practical introductIOn to the discrete Kalman
filter. This introductIOn includes a description and some discussion of the basic
discrete Kalman filter, a derivation, description and some discussion of the extended
Kalman filter, and a relatively simple (tangible) example with real numbers &
results.
