LinCAN is a Linux kernel module that implements a CAN driver capable of working with multiple cards, even with different chips and IO methods. Each communication object can be accessed from multiple applications concurrently.
It supports RT-Linux, 2.2, 2.4, and 2.6 with fully implemented select, poll, fasync, O_NONBLOCK, and O_SYNC semantics and multithreaded read/write capabilities. It works with the common Intel i82527, Philips 82c200, and Philips SJA1000 (in standard and PeliCAN mode) CAN controllers.
LinCAN project is part of a set of CAN/CANopen related components developed as part of OCERA framework.
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.
