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Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, i...

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Bibliographic Details
Main Author: Solé-Casals, Jordi
Other Authors: Sun, Zhe, Caiafa, Cesar F., Marti-Puig, Pere
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Machine Learning
Discrete Fourier Transform (dft)
Pairwise Evaluation
Artificial Neural Network
Data Augmentation
Feature Selection
Deep Learning
Hierarchical Clustering
Policy-making Support
Few-shot Learning
Permutation-variable Importance
Tensor Completion
Social Vulnerability
Functional Connectivity
Information Technology Industries / Bicssc
Ontology
Weighted Interpolation Map
Root Canal Measurement
Feature Engineering
Similarly Shaped Fish Species
Sound Event Detection
Graph Model
Prediction
Persistent Entropy
Single Sample Per Person
Episodic Memory
Sparse Representations
Semi-supervised Learning
Discriminant Analysis
Non-negative Matrix Factorization
Internet Of Things (iot)
Noisy Datasets
Functional Magnetic Resonance Imaging
Tensor Decomposition
Covid19
Noise Elimination
Machine Translation
Gender-gap
Small Data-sets
Binarization
Small Datasets
Empirical Mode Decomposition
Data Science
Smart Building
Open Contours
Topological Data Analysis
Markov Chain Monte Carlo (mcmc)
Image Generation
Multifrequency Impedance
Independent Component Analysis
Discrete Cosine Transform (dct)
Feature Extraction
Extreme Learning Machines (elm)
Feature Elimination
Digital-gap
Preprocessing
Auto-encoders
Shadow Estimation
Small Sample Learning
Intelligent Decision Support
Shadow Detection
Feature Importance
Recurrent Neural Network
Imperfect Dataset
Ultrasound Images
Support-vector Machine
Artificial Intelligence
Parkinson's Disease
Label Correlations
Dengue
Space Consistency
Optimization
Neural Network
Educational Data
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (316 p.)
ISBN:9783036512884
books978-3-0365-1287-7
9783036512877

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