Search results
Results from the WOW.Com Content Network
Online algorithm. In computer science, an online algorithm[1] is one that can process its input piece-by-piece in a serial fashion, i.e., in the order that the input is fed to the algorithm, without having the entire input available from the start. In contrast, an offline algorithm is given the whole problem data from the beginning and is ...
Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...
A famous online problem where a decision is made only once is the Ski rental problem. In general, the output of an online algorithm is compared to the solution of a corresponding offline algorithm which is necessarily always optimal and knows the entire input in advance (competitive analysis).
Competitive analysis is a method invented for analyzing online algorithms, in which the performance of an online algorithm (which must satisfy an unpredictable sequence of requests, completing each request without being able to see the future) is compared to the performance of an optimal offline algorithm that can view the sequence of requests in advance.
The bin packing problem[1][2][3][4] is an optimization problem, in which items of different sizes must be packed into a finite number of bins or containers, each of a fixed given capacity, in a way that minimizes the number of bins used. The problem has many applications, such as filling up containers, loading trucks with weight capacity ...
In mathematics and computer science, an algorithm (/ ˈælɡərɪðəm / ⓘ) 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.
Algorithm selection (sometimes also called per-instance algorithm selection or offline algorithm selection) is a meta- algorithmic technique to choose an algorithm from a portfolio on an instance-by-instance basis. It is motivated by the observation that on many practical problems, different algorithms have different performance characteristics.
Offline learning is working in batch mode. In step 1 the task is demonstrated and stored in the table, and in step 2 the task is reproduced by the robot. [4] The pipeline is slow and inefficient because a delay is there between behavior demonstration and skill replay. [5][6] A short example will help to understand the idea.