Objective: This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms. Methods: A ...
"code": "def fibonacci(n):\n \"\"\"Fast doubling Fibonacci - O(log n) time.\"\"\"\n def _fast_fib(k):\n if k == 0:\n return (0, 1)\n a, b = _fast_fib(k // 2)\n c = a ...
"code": "def fibonacci(n):\n if n <= 0:\n return 0\n if n == 1:\n return 1\n a, b = 0, 1\n for _ in range(2, n + 1):\n a, b = b, a + b\n return b" "iteration": 1 ...
Abstract: The success of most robust model estimation methods heavily relies on their used data sampling algorithms. This letter proposes a novel sampling algorithm, called Guided Sampling by ...
Abstract: In complex process industries, multivariate time sequences are omnipresent, whose nonlinearities and dynamics present two major challenges for soft sensing of important quality variables.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results