Abc-boost is a new line of boosting algorithms for multi-class classification, by utilizing the commonly used sum-to-zero constraint. To implement abc-boost, a base class must be identified at each boosting step. Prior studies used a very expensive procedure based on exhaustive search for determining the base class at …
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5 results for “ABC-Boost”
A new framework for selecting base classes in multi-class classification boosts accuracy.
problem Selecting the base class in multi-class classification to improve accuracy.
method Introduces a unified framework with parameters to search for the base class at each boosting iteration, improving computational efficiency.
result Our framework can achieve better test accuracy than the exhaustive search strategy, providing a robust and reliable scheme.
Package for Fast ABC-Booststat.ML
Package implements ABC-Boost for multi-class classification.
problem Improving accuracy in multi-class classification.
method Combines histogram-based binning and explicit gain formula for efficient tree splitting.
result ABC-Boost significantly improves multi-class classification accuracy.
Logitboost is an influential boosting algorithm for classification. In this paper, we develop robust logitboost to provide an explicit formulation of tree-split criterion for building weak learners (regression trees) for logitboost. This formulation leads to a numerically stable implementation of logitboost. We then pr…
pGMM kernel outperforms ordinary ridge regression and RBF kernel ridge regression without tuning.
problem Comparing pGMM kernel regression with other ridge regression methods.
method Implemented and compared pGMM kernel regression with ordinary ridge regression and RBF kernel ridge regression.
result pGMM kernel performs well without tuning and can match boosted trees with parameter tuning.