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Accuracy and multi-core performance of machine learning algorithms for handwritten character recognition.

机译：用于手写字符识别的机器学习算法的准确性和多核性能。

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摘要

There have been considerable developments in the quest for intelligent machines since the beginning of the cybernetics revolution and the advent of computers. In the last two decades with the onset of the internet the developments have been extensive. This quest for building intelligent machines have led into research on the working of human brain, which has in turn led to the development of pattern recognition models which take inspiration in their structure and performance from biological neural networks. Research in creating intelligent systems poses two main problems. The first one is to develop algorithms which can generalize and predict accurately based on previous examples. The second one is to make these algorithms run fast enough to be able to do real time tasks. The aim of this thesis is to study and compare the accuracy and multi-core performance of some of the best learning algorithms to the task of handwritten character recognition. Seven algorithms are compared for their accuracy on the MNIST database, and the test set accuracy (generalization) for the different algorithms are compared. The second task is to implement and compare the performance of two of the hierarchical Bayesian based cortical algorithms, Hierarchical Temporal Memory (HTM) and Hierarchical Expectation Refinement Algorithm (HERA) on multi-core architectures. The results indicate that the HTM and HERA algorithms can make use of the parallelism in multi-core architectures.
机译：自控制论革命开始和计算机问世以来，对智能机器的需求已取得了长足的发展。在过去的二十年中，随着互联网的出现，发展得到了广泛的发展。对构建智能机器的追求导致了对人脑工作的研究，进而导致了模式识别模型的开发，该模型从生物神经网络的结构和性能中获得了灵感。创建智能系统的研究存在两个主要问题。第一个是根据先前的示例，开发出可以准确归纳和预测的算法。第二个方法是使这些算法运行得足够快，以能够执行实时任务。本文的目的是研究和比较一些最佳学习算法的准确性和多核性能与手写字符识别任务。在MNIST数据库上比较了七个算法的准确性，并比较了不同算法的测试集准确性（一般化）。第二项任务是在多核架构上实现并比较基于贝叶斯的分层皮质算法（HTM）和期望期望细化算法（HERA）中的两种。结果表明，HTM和HERA算法可以在多核体系结构中利用并行性。

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