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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:
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 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.
The learning rate is the ratio (percentage) that influences the speed and quality of learning. The greater the ratio, the faster the neuron trains, but the lower the ratio, the more accurate the training.
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
A learning algorithm over is a computable map from to . In other words, it is an algorithm that takes as input a finite sequence of training samples and outputs a function from X {\displaystyle X} to Y {\displaystyle Y} .