Robust regression and outlier detection
Robust regression and outlier detection
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A method for simultaneous variable selection and outlier identification in linear regression
Computational Statistics & Data Analysis
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Computing least trimmed squares regression with the forward search
Statistics and Computing
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
A Comparative Study of RNN for Outlier Detection in Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Fast cross-validation of high-breakdown resampling methods for PCA
Computational Statistics & Data Analysis
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In the context of resistant learning, outliers are the observations far away from the fitting function that is deduced from a subset of the given observations and whose form is adaptable during the process. This study presents a resistant learning procedure for coping with outliers via single-hidden layer feed-forward neural network (SLFN). The smallest trimmed sum of squared residuals principle is adopted as the guidance of the proposed procedure, and key mechanisms are: an analysis mechanism that excludes any potential outliers at early stages of the process, a modeling mechanism that deduces enough hidden nodes for fitting the reference observations, an estimating mechanism that tunes the associated weights of SLFN, and a deletion diagnostics mechanism that checks to see if the resulted SLFN is stable. The lake data set is used to demonstrate the resistant-learning performance of the proposed procedure.