The phrase "adaptive boost" is correct and usable in written English.
It can be used in contexts related to technology, machine learning, or any field where adjustments or enhancements are made to improve performance or efficiency.
Example: "The new software update includes an adaptive boost feature that optimizes processing speed based on user behavior."
Alternatives: "dynamic enhancement" or "responsive improvement".
This paper proposes a new technique for power quality assessment, using rule-based S-Transform (ST) as a feature extraction tool and Adaptive Boost (AdaBoost) as a classifier.
An efficient boosting algorithm named adaptive boost (AdaBoost) was proposed to solve many of the practical difficulties of earlier boosting algorithms [10].
Similarly, another approach, miRdup [ 19], uses Random Forest algorithm integrated with Adaptive Boost (Adaboost) and can be used to detect the positions of miRNAs based on features from five lineages of cleavage sites on the miRNA:miRNA ∗ duplexes.
For example, the boosted decision trees [6] combines each decision tree successively in an adaptive boosting manner, and Boosted Fuzzy-Rule-Based Classifiers [7]combines each fuzzy rule successively in an adaptive boosting way.
ADABOOST (adaptive boosting algorithm) is one of the most used boosting algorithms which automatically adapts to the data given to it.
This paper presents an FPGA architecture for objects classification based on Adaptive Boosting algorithm.
A comparative study with another approach which exploits differently estimation errors, namely adaptive boosting for regression (AdaBoost.R), is also included.
The original adaptive boosting algorithm and its application in face detection and facial expression recognition are reviewed.
The researchers use a statistical classification approach called adaptive boosting to train the system to identify the marker in environments with a high degree of visual noise.
Adaptive Boosting (AdaBoost) is an ensemble algorithm with meta-estimators, which initially fits on a sequence of weak learners with repeatedly modified versions of the data.
Eye movement directions in response to a visual stimulus for tracking an object are classified using ensemble classifiers based on bagging and adaptive boosting algorithms.
