Machine learningFinancial fraudMeta-ClassifiersVoting-ClassifierStacked-ClassifierWe develop Meta-Classifiers to detect financial frauds by combining several accurate and diverse stand-alone classifiers. Our results suggest that the Meta-Classifiers developed in our study can outperform the best stand-alone ...
适应性可以理解为,给定训练数据集训练的模型,却可以接近类似的不同的问题的能力。 换言之, 元学习是 让模型具有接近新task的能力, 也可以称之为learning to learn。 一下提供两个简单的例子来理解什么是元学习: 一个分类器(classifier)只在没有猫咪的图片集上进行了训练,但是输入一张猫咪的图片可以分辨此图是否...
In order to induce a meta classifier, first the base classifiers are trained (stage one), and then the Meta classifier (second stage). In the prediction phase, base classifiers will output their classifications, and then the Meta-classifier(s) will make the final classification (as a function...
The base-learner can be any binary classifier, and the meta-learner is a deep RL network consisting of a deep neural network that learns a representation of the AL problem across tasks, and a policy network that learns the optimal policy, parameterized as weights in the network. The meta-le...
Meta-Learning for Multi-label Classification 需要解决的一个不可微分的问题(threshold 这里有个对梯度的截断),所以文章提出用 Policy gradient 的方法来训练这个 classifier,设计了一个跟预测的 threshold 以及 prediction label prob 的相关的 reward,具体请参考文章中的公式,一句话说就是分类正确并且和预测的 label...
information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except ...
Explaining in Style: Training a GAN to explain a classifier in StyleSpace Automated Reinforcement Learning (AutoRL): A Survey and Open Problems An Introduction to Autoencoders Vision Transformer with Deformable Attention Pushing the limits of self-supervised ResNets: Can we outperform supervised learnin...
These methods use conditional probability as the core of meta-learning computations and modify the classifier to pick up new classes using pre-trained networks. With the development of meta-learning, many novel models and variants have emerged in recent years. Besides the above three types of ...
user ("You are a sentiment classifier. For each message, give the percentage of positive/netural/negative."), user ("I liked it"), assistant ("70% positive 30% neutral 0% negative"), user ("It could be better"), assistant ("0% positive 50% neutral 50% negative"), ...
Defining fθ as a classifier, parameter θ extracts the probability of data points belonging to category y, which is given by the following attribute vector x, Pθ(y∣x). The optimal parameters should maximize the probability of identifying the true label in multiple training batches B ⊂ D...