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A stable learning algorithm would produce a similar classifier with both the 1000-element and 999-element training sets. Stability can be studied for many types of learning problems, from language learning to inverse problems in physics and engineering, as it is a property of the learning process rather than the type of information being learned.
In computer science, a stable sorting algorithm preserves the order of records with equal keys. In numerical analysis, a numerically stable algorithm avoids magnifying small errors. An algorithm is stable if the result produced is relatively insensitive to perturbations during computation.
This algorithm is guaranteed to produce a stable marriage for all participants in time where is the number of men or women. [11] Among all possible different stable matchings, it always yields the one that is best for all men among all stable matchings, and worst for all women. [12]
An internetwork is the connection of multiple different types of computer networks to form a single computer network using higher-layer network protocols and connecting them together using routers. The Internet is the largest example of internetwork. It is a global system of interconnected governmental, academic, corporate, public, and private ...
Download as PDF; Printable version; In other projects ... move to sidebar hide. Help. Algorithms for Computer System. Subcategories. This category has the following 2 ...
In mathematics and computer science, an algorithm (/ ˈ æ l ɡ ə r ɪ ð əm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation. [1] Algorithms are used as specifications for performing calculations and data processing.
Stable sorting algorithms maintain the relative order of records with equal keys (i.e. values). That is, a sorting algorithm is stable if whenever there are two records R and S with the same key and with R appearing before S in the original list, R will appear before S in the sorted list.
An algorithm for solving a linear evolutionary partial differential equation is stable if the total variation of the numerical solution at a fixed time remains bounded as the step size goes to zero. The Lax equivalence theorem states that an algorithm converges if it is consistent and stable (in this sense).