By R. A. Hill

The purpose of this publication is to supply an basic therapy of the speculation of error-correcting codes, assuming not more than highschool arithmetic and the facility to hold out matrix mathematics. The booklet is meant to function a self-contained direction for moment or 3rd yr arithmetic undergraduates, or as a readable creation to the mathematical points of coding for college kids in engineering or machine technological know-how.

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**Extra info for A First Course in Coding Theory**

**Example text**

For each example, the actual decision model predicts yˆi , that can be either True (yˆi = yi ) or False (yˆi = yi ). For a set of examples, the error is a random variable from Bernoulli trials. The binomial distribution gives the general form of the probability for the random variable that represents the number of errors in a sample of n examples. For each point i in the sequence, the error-rate is the probability of observing False, pi , with standard deviation given by si = pi (1 − pi )/i. The drift detection method manages two registers during the training of the learning algorithm, pmin and smin .

Algorithm 7: The Monitoring Threshold Functions Algorithm (sensor node). 4 Notes The research on Data Stream Management Systems started in the database community, to solve problems like continuous queries in transient data. 8: The bounding theorem. The convex-hull of sensors is bounded by the union of spheres. Sensors only need to communicate their measurements when the spheres are non-monochromatic. most relevant projects include: The Stanford StREam DatA Manager (Stanford University) with focus on data management and query processing in the presence of multiple, continuous, rapid, time-varying data streams.

If the process is not strictly stationary (as most of real-world applications), the target concept could change over time. Nevertheless, most of the work in Machine Learning assumes that training examples are generated at random according to some stationary probability distribution. Basseville and Nikiforov (1993) present several examples of real problems where change detection is relevant. These include user modeling, monitoring in bio-medicine and industrial processes, fault detection and diagnosis, safety of complex systems, etc.