论文标题
在多层感知器分类器中实现冰估计器
Implementing the ICE Estimator in Multilayer Perceptron Classifiers
论文作者
论文摘要
本文介绍了用于实现多层感知模型的ICE估计器的技术，并回顾了所得模型的性能。冰估计器在Apache Spark MultilayerPercePtronClassifier中实现，并以交叉验证显示，以胜过使用未调整的MLE（交叉透明镜）损失的库存多层MultilayerPerceptronClassifier。最终的模型具有相同的运行时性能，并且与库存MLP实现相似。此外，这种方法不需要超级参数，因此可行，作为换入式替代品，用于优化多层观察者分类器，无论在任何地方都可能引起人们的关注。
This paper describes the techniques used to implement the ICE estimator for a multilayer perceptron model, and reviews the performance of the resulting models. The ICE estimator is implemented in the Apache Spark MultilayerPerceptronClassifier, and shown in cross-validation to outperform the stock MultilayerPerceptronClassifier that uses unadjusted MLE (cross-entropy) loss. The resulting models have identical runtime performance, and similar fitting performance to the stock MLP implementations. Additionally, this approach requires no hyper-parameters, and is therefore viable as a drop-in replacement for cross-entropy optimizing multilayer perceptron classifiers wherever overfitting may be a concern.
