This article aims to present the reader to the current workings of the Accord.NET Machine Learning Framework; show where the sequences classifiers are located within the framework, describe their source code, how the Markov namespace is organized and the general ideas behind this organization. This will also provide the base to talk about Hidden Conditional Random Fields, which is my main goal in this series.
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Computer and technology enthusiast, interested in artificial intelligence and image processing. Has a Master's degree on Computer Science specialized on Image and Signal Processing, with expertise on Machine Learning, Computer Vision, Pattern Recognition and Data Mining systems. Author of the
Accord.NET Framework for developing scientific computing applications.
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