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Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large
Classification problems having thousands or more classes naturally occur in NLP, for example language models or document classification.
Towards Better Decision Forests: Forest Alternating Optimization
However, unlike for most other models, such as neural networks, optimizing forests or trees is not easy, because they define a non-differentiable function.
Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees
Ensemble methods based on decision trees, such as Random Forests or boosted forests, have long been established as some of the most powerful, off-the-shelf machine learning models, and have been widely used in computer vision and other areas.
Faster Neural Net Inference via Forests of Sparse Oblique Decision Trees
We show that neural nets can be further compressed by replacing layers of it with a special type of decision forest.
An Experimental Comparison of Old and New Decision Tree Algorithms
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algorithms.
