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Machine learning
TLDR
Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Ensemble Methods in Machine Learning
TLDR
Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
Solving Multiclass Learning Problems via Error-Correcting Output Codes
TLDR
It is demonstrated that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
TLDR
This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
TLDR
This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task and measures the power (ability to detect algorithm differences when they do exist) of these tests.
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
TLDR
The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges with probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the five kinds of state abstraction.
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
TLDR
The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting, and sometimes better than randomization.
Deep Anomaly Detection with Outlier Exposure
TLDR
In extensive experiments on natural language processing and small- and large-scale vision tasks, it is found that Outlier Exposure significantly improves detection performance and that cutting-edge generative models trained on CIFar-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; OE is used to mitigate this issue.
Pruning Adaptive Boosting
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