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Methods to achieve this task are varied and span many disciplines; most well known among them are machine learning and statistics. Classification and prediction tasks aim at building models that describe and distinguish classes or concepts for future prediction. The differences between them are the following:
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The adaptive mixtures of local experts [5] [6] uses a gaussian mixture model.Each expert simply predicts a gaussian distribution, and totally ignores the input. Specifically, the -th expert predicts that the output is (,), where is a learnable parameter.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
Knowledge distillation consists of training a smaller network, called the distilled model, on a data set called the transfer set (which is different than the data set used to train the large model) using cross-entropy as the loss function between the output of the distilled model (|) and the output of the large model ^ (|) on the same record ...
[4] [5] The difference between these and NLP is that the latter builds up a single program or a library of routines that are programmed through natural language sentences using an ontology that defines the available data structures in a high level programming language. An example text from an English language natural-language program is as follows:
Neural networks are typically trained through empirical risk minimization.This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. [4]
Many real-world applications fall between the two extremes, for instance text classification for the automatic analysis of emails and their routing to a suitable department in a corporation does not require an in-depth understanding of the text, [22] but needs to deal with a much larger vocabulary and more diverse syntax than the management of ...