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Meta-learning [1] [2] is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing ...
Microsoft SEAL comes with two different homomorphic encryption schemes with very different properties: BFV : [ 5 ] The BFV scheme allows modular arithmetic to be performed on encrypted integers. For applications where exact values are necessary, the BFV scheme is the only choice.
[1] [2] The algorithm assumes that we have no prior knowledge about the accuracy of the algorithms in the pool, but there are sufficient reasons to believe that one or more will perform well. Assume that the problem is a binary decision problem. To construct the compound algorithm, a positive weight is given to each of the algorithms in the pool.
At the same time, neural networks offer a new approach to attack ciphering algorithms based on the principle that any function could be reproduced by a neural network, which is a powerful proven computational tool that can be used to find the inverse-function of any cryptographic algorithm. The ideas of mutual learning, self learning, and ...
Angluin's work on learning from noisy examples [13] has also been very influential to the field of machine learning. [10] Her work addresses the problem of adapting learning algorithms to cope with incorrect training examples . Angluin's study demonstrates that algorithms exist for learning in the presence of errors in the data. [10]
The following pseudo code would correspond to this general definition of an MA: Pseudo code Procedure Memetic Algorithm Initialize: Generate an initial population, evaluate the individuals and assign a quality value to them; while Stopping conditions are not satisfied do Evolve a new population using stochastic search operators.
In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. [1] It is based on the idea of representing secret information as a set of equations with errors. In other words, LWE is a way to hide the value of a secret by introducing noise to it. [2]
In cryptography, SEAL (Software-Optimized Encryption Algorithm) is a stream cipher optimised for machines with a 32-bit word size and plenty of RAM with a reported performance of around 4 cycles per byte.