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For example, the individual components of a differential white blood cell count must all add up to 100, because each is a percentage of the total. Data that is embedded in narrative text (e.g., interview transcripts) must be manually coded into discrete variables that a statistical or machine-learning package can deal with.
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.
The Yarowsky algorithm is an example of self-supervised learning in natural language processing. From a small number of labeled examples, it learns to predict which word sense of a polysemous word is being used at a given point in text. DirectPred is a NCSSL that directly sets the predictor weights instead of learning it via typical gradient ...
For example, a one-instruction set computer (OISC) machine that uses only the subtract-and-branch-if-negative "instruction" cannot do an indirect copy (something like the equivalent of "*a = **b" in the C language) without using self-modifying code. Booting. Early microcomputers often used self
The aim of a self-organizing list is to improve efficiency of linear search by moving more frequently accessed items towards the head of the list. A self-organizing list achieves near constant time for element access in the best case. A self-organizing list uses a reorganizing algorithm to adapt to various query distributions at runtime.
Self-tuning metaheuristics have emerged as a significant advancement in the field of optimization algorithms in recent years, since fine tuning can be a very long and difficult process. [3] These algorithms differentiate themselves by their ability to autonomously adjust their parameters in response to the problem at hand, enhancing efficiency ...
Large margin nearest neighbor (LMNN) [1] classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming , a sub-class of convex optimization .
An example of an adaptive algorithm in radar systems is the constant false alarm rate (CFAR) detector. In machine learning and optimization , many algorithms are adaptive or have adaptive variants, which usually means that the algorithm parameters such as learning rate are automatically adjusted according to statistics about the optimisation ...