Machine learning classification models which are robust against label noise are provided. Noise may be modelled explicitly by modelling "label flips", where incorrect binary labels are "flipped" relative to their ground truth value. Distributions of label flips may be modelled as prior and posterior...
any classifier. Yup, you can use sklearn/pyTorch/Tensorflow/FastText/etc.lnl=LearningWithNoisyLabels(clf=LogisticRegression())lnl.fit(X=X_train_data,s=train_noisy_labels)# Estimate the predictions you would have gotten by training with *no* label errors.predicted_test_labels=lnl.predict(X_...
This Machine Learning tutorial is for anyone who wants to learn about machine learning. No prior knowledge of machine learning is required. Read the tutorial to learn more about machine learning.
Machine learning is generally divided into supervised learning, as illustrated in Fig. 3.7. This consists of labeled data, where the algorithm receives a set of labeled data, that is, a set of inputs together with the respective correct outputs, causing the algorithm to learn by making comparis...
In this document, we survey hundreds of survey papers on Natural Language Processing (NLP) and Machine Learning (ML). We categorize these papers into popular topics and do simple counting for some interesting problems. In addition, we show the list of the papers with urls (813 papers). Categ...
Although data mining can provide a great deal of prediction power, some models, such as random forest and deep-learning neural networks, are so complex that it is unclear how the input variables are connected with the output. It is sometimes unattainable to derive causal relationships from the ...
Learning Soft Labels via Meta Learning One-hot labels do not represent soft decision boundaries among concepts, and hence, models trained on them are prone to overfitting. Using soft labels as targets provide regularization, but different soft labels might be optimal at different stages of optimizati...
study for in-orbit flood mapping using low-cost hardware with machine learning capability to reduce the amount of data required to be downlinked. This concept will enable the use of large cubesat constellation to reliable monitor environmental phenomena such as flooding with high temporal resolution....
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey. arXiv 2020 paper bib Andrea Borghesi, Federico Baldo, Michela Milano Learning from Noisy Labels with Deep Neural Networks: A Survey. arXiv 2020 paper bib Hwanjun Song, Minseok Kim, Dongmin Park, Jae-Gil Lee...
The integration of big data technologies and cloud computing with machine learning is set to revolutionize data mining. Cloud platforms provide the necessary infrastructure to store and process vast amounts of data, while big data technologies enable the analysis of complex datasets in real-time. ...