首页|Findings on Machine Learning Reported by Investigators at University of Californ ia San Diego (UCSD) (Randomly Pivoted Cholesky: Practical Approximation of a Ker nel Matrix With Few Entry Evaluations)
Findings on Machine Learning Reported by Investigators at University of Californ ia San Diego (UCSD) (Randomly Pivoted Cholesky: Practical Approximation of a Ker nel Matrix With Few Entry Evaluations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators publish new report on Ma chine Learning. According to news reportingout of La Jolla, California, by News Rx editors, research stated, “The randomly pivoted Cholesky algorithm(RPCholesk y) computes a factorized rank-k$k$ approximation of a n NxN$N \times N$ positivesemidefinite(psd) matrix. RPCholesky requires only (k+1)N$(k + 1)N $ entry evaluations and O(k2N)$mathcal {O}(k <. > 2 N)$ additional arithmetic operations, and it can be implemented with just a fewlines of code.”
La JollaCaliforniaUnited StatesNor th and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniver sity of California San Diego (UCSD)
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