"At the highest level of generality, a general CBR cycle may be described by the following four processes:
– Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Agnar Aamodt & Enric Plaza. AI Communications. IOS Press, Vol. 7: 1, pp. 39-59.
Once we have formed our training data-set, which is represented as an M x N matrix where M is the number of data points and N is the number of features, we can now begin classifying. There are two important decisions that must be made before making classifications. One is the value of k that will be used; this can either be decided arbitrarily, or you can try cross-validation to find an optimal value. The next, and the most complex, is the distance metric that will be used. There are many different ways to compute distance, as it is a fairly ambiguous notion, and the proper metric to use is always going to be determined by the data-set and the classification task.
Then you compare a number of ANN algorithms on that on a number of data-sets (each comes with a pre-selected distance metric for the exhaustive nearest neighbour search, while the approximative algorithms may or may not use the same metric but are compared to it). I have indeed not seen many empirical tests comparing the approximate nearest neighbour to the actual precomputed nearest neighbour. What I have seen however are empirical tests where you use nearest neighbour search as a subroutine in some classification algorithm or other and where the classification performance results of the algorithm with (impractical) exhaustive search are tested against a variant with approximative search. Do you think testing only ANN against exhaustive 1NN is sufficient no matter what the techniques get used for? Apparently, you don't think just testing it through the outcome in the application scenario is sufficient, but maybe both then?
Machine Learning is one of the most popular approaches in Artificial Intelligence. Over the past decade, Machine Learning has become one of the integral parts of our life. It is implemented in a task as simple as recognizing human handwriting or as complex as self-driving cars. It is also expected that in a couple of decades, the more mechanical repetitive task will be over. With the increasing amounts of data becoming available there is a good reason to believe that Machine Learning will become even more prevalent as a necessary element for technological progress. There are many key industries where ML is making a huge impact: Financial services, Delivery, Marketing and Sales, Health Care to name a few. However, here we will discuss the implementation and usage of Machine Learning in trading.