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Machine learning for branch prediction using LVQ and multi-layer perceptrons, called "neural branch prediction", was proposed by Lucian Vintan (Lucian Blaga University of Sibiu). [24] One year later he developed the perceptron branch predictor. [25] The neural branch predictor research was developed much further by Daniel Jimenez. [26]
Branch target prediction is not the same as branch prediction, which guesses whether a conditional branch will be taken or not-taken in a binary manner. In more parallel processor designs, as the instruction cache latency grows longer and the fetch width grows wider, branch target extraction becomes a bottleneck. The recurrence is:
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the Probably Approximately Correct Learning (PAC) model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the ...
Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature. Statistical algorithms can further be categorized as generative or discriminative .
Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
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The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [34] [35] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot ...