60 results on '"Figueiras-Vidal, Aníbal R."'
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2. Machine-Health Application Based on Machine Learning Techniques for Prediction of Valve Wear in a Manufacturing Plant
3. A Principled Two-Step Method for Example-Dependent Cost Binary Classification
4. Neural network for ordinal classification of imbalanced data by minimizing a Bayesian cost
5. Imbalance example-dependent cost classification: A Bayesian based method
6. Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles
7. Pre-emphasizing Binarized Ensembles to Improve Classification Performance
8. Values Deletion to Improve Deep Imputation Processes
9. Walsh Sequences as Direct Error Correcting Ouput Code Dichotomies for Multiclass Problems
10. Imbalance Example-Dependent Cost Classification: A Bayesian Based Method
11. Classification of Binary Imbalanced Data Using A Bayesian Ensemble of Bayesian Neural Networks
12. On the design of Bayesian principled algorithms for imbalanced classification
13. Smoothed Emphasis for Boosting Ensembles
14. Complete Stacked Denoising Auto-Encoders for Regression
15. MNIST-NET10: A heterogeneous deep networks fusion based on the degree of certainty to reach 0.1% error rate. Ensembles overview and proposal
16. Complete autoencoders for classification with missing values
17. Corrigendum to “Likelihood ratio equivalence and imbalanced binary classification” [Expert Systems with Applications, Volume 130 (2019), Pages 84--96]
18. Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification
19. Exploiting label information to improve auto-encoding based classifiers
20. On improving CNNs performance: The case of MNIST
21. Exploiting Multitask Learning Schemes Using Private Subnetworks
22. Support Vector Method for ARMA System Identification: A Robust Cost Interpretation
23. Multi-dimensional Function Approximation and Regression Estimation
24. Support Vector Robust Algorithms for Non- parametric Spectral Analysis
25. Boundary Methods for Distribution Analysis
26. Channel Equalization with Neural Networks
27. Digital Equalization Using Modular Neural Networks: an Overview
28. Likelihood ratio equivalence and imbalanced binary classification
29. Improving deep learning performance with missing values via deletion and compensation
30. Nonlinear time series modeling by competitive segmentation of state space
31. Optimal blind equalization of Gaussian channels
32. A FAST RLS LOOK-UP-TABLE PLUS FIR ADAPTIVE ECHO CANCELLER
33. Linear discriminants described by disjoint tangent configurations
34. Training neural network classifiers through Bayes risk minimization applying unidimensional Parzen windows
35. On building ensembles of stacked denoising auto-encoding classifiers and their further improvement – a correction
36. On building ensembles of stacked denoising auto-encoding classifiers and their further improvement
37. Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets
38. Boosting ensembles with controlled emphasis intensity
39. Post-aggregation of classifier ensembles
40. A new boosting design of Support Vector Machine classifiers
41. A Single Layer Perceptron Approach to Selective Multi-task Learning
42. Multi-task Neural Networks for Dealing with Missing Inputs
43. Classification with Incomplete Data
44. Neighborhood Guided Smoothed Emphasis for Real Adaboost Ensembles
45. Classifying patterns with missing values using Multi-Task Learning perceptrons
46. Adaptively Biasing the Weights of Adaptive Filters
47. Committees of Adaboost ensembles with modified emphasis functions
48. Pattern classification with missing data: a review
49. Adaptive Combination of Proportionate Filters for Sparse Echo Cancellation
50. K nearest neighbours with mutual information for simultaneous classification and missing data imputation
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