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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 ...
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.
Learning algorithm: Numerous trade-offs exist between learning algorithms. Almost any algorithm will work well with the correct hyperparameters [163] for training on a particular data set. However, selecting and tuning an algorithm for training on unseen data requires significant experimentation.
In computer architecture, predication is a feature that provides an alternative to conditional transfer of control, as implemented by conditional branch machine instructions. Predication works by having conditional ( predicated ) non-branch instructions associated with a predicate , a Boolean value used by the instruction to control whether the ...
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 ...
Predictive learning is a machine learning (ML) technique where an artificial intelligence model is fed new data to develop an understanding of its environment, capabilities, and limitations. This technique finds application in many areas, including neuroscience , business , robotics , and computer vision .