By James H. Wilkinson
Ordinary advent to challenge of cumulative impact of rounding error in a really huge variety of arithmetical calculations—particularly acceptable to computing device operations. uncomplicated consultant analyses illustrate concepts. themes comprise basic mathematics operations, computations concerning polynomials and matrix computations. effects deal completely with electronic pcs yet are both acceptable to table calculators. Bibliography.
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Extra info for Rounding errors in algebraic processes
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.