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OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
Graphs of functions commonly used in the analysis of algorithms, showing the number of operations N versus input size n for each function. Insertion sort applied to a list of n elements, assumed to be all different and initially in random order. On average, half the elements in a list A 1... A j are less than element A j+1, and half are greater.
Thus, a representation that compresses the storage size of a file from 10 MB to 2 MB yields a space saving of 1 - 2/10 = 0.8, often notated as a percentage, 80%. For signals of indefinite size, such as streaming audio and video, the compression ratio is defined in terms of uncompressed and compressed data rates instead of data sizes:
Thus, the α-EM algorithm by Yasuo Matsuyama is an exact generalization of the log-EM algorithm. No computation of gradient or Hessian matrix is needed. The α-EM shows faster convergence than the log-EM algorithm by choosing an appropriate α. The α-EM algorithm leads to a faster version of the Hidden Markov model estimation algorithm α-HMM ...
Computer programming or coding is the composition of sequences of instructions, called programs, that computers can follow to perform tasks. [1] [2] It involves designing and implementing algorithms, step-by-step specifications of procedures, by writing code in one or more programming languages.
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. [1]
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The forward–backward algorithm runs with time complexity () in space (), where is the length of the time sequence and is the number of symbols in the state alphabet. [1] The algorithm can also run in constant space with time complexity O ( S 2 T 2 ) {\displaystyle O(S^{2}T^{2})} by recomputing values at each step. [ 2 ]