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What is rls algorithm?
The Recursive least squares RLS adaptive filter is an algorithm which recursively finds the filter coefficients that minimize a weighted linear least squares cost function relating to the input signals. This is in contrast to other algorithms such as the least mean squares LMS that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.
What is sub-algorithm?
It is an algorithm used by another algorithm as part of the second algorithm's operation.As an example, an algorithm for finding the median value in a list of numbers might include sorting the numbers as a sub-algorithm: There are plenty of algorithms for sorting, and the specifics of the sorting does not matter to the "median value" algorithm, only that the numbers are sorted when the sub-algorithm is done.For what an algorithm is, see related link.
What is Dijkstra's algorithm?
Dijkstra's algorithm is used by the OSPF and the IS-IS routing protocols. The last three letters in OSPF (SPF) mean "shortest path first", which is an alternative name for Dijkstra's algorithm.
Difference between K-mean and K-medoids algorithm for clustering techniques in data mining?
Both of them utilize expectation-maximization strategy to converge to a minimum error condition. While K-Medoids require the cluster centters to be centroids, in k-Means the centers could be anywhere in the sample space. k-Medoids is more robust to outliners than k-Means therefore results in more quality clustering. It is also computationally more complex.
Who developed an algorithm?
Here are some of the first we know of:* Babylonians, 1600 BC - factorization and square roots* Euclid, 300 BC - greatest common divisor (GCD)* Eratosthenes, 200 BC - prime numbers* Liu Hui, 263 AD - systems of linear equationsSee related link.
