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Brain-computer interfaces. --- Decoders (Electronics) --- Electroencephalography. --- Medical technology. --- Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Health care technology --- Health technology --- Technology --- EEG --- Encephalography --- Brain --- Electrodiagnosis --- Electrophysiology --- Visual evoked response --- Coding theory --- BCIs (Brain-computer interfaces) --- Brain-machine interfaces --- Computer-brain interfaces --- Direct neural interfaces --- User interfaces (Computer systems) --- Diseases --- Diagnosis
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Mass communications --- Information systems --- Computer. Automation --- beeldverwerking --- signal processing --- database management --- communicatietechnologie --- signaalverwerking
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This state-of-the-art resource brings together the latest findings from the cross-fertilization of signal processing, machine learning and computer science. The emphasis is on demonstrating synergy of different signal processing methods with knowledge extraction and heterogeneous information fusion. Issues related to the processing of signals with low signal-to-noise ratio, solving real-world multi-channel problems, and using adaptive techniques where nonstationarity, uncertainty and complexity play major roles are addressed. Particular methods include Independent Component Analysis, Support Vector Machines, Distributed and Collaborative Adaptive Filtering, Empirical Mode Decomposition, Self Organizing Maps, Fuzzy Logic, Evolutionary Algorithms and several others used frequently in these fields. Also included are both important and novel applications from telecommunications, renewable energy and biomedical engineering. Signal Processing Techniques for Knowledge Extraction and Information Fusion which proposes new techniques for extracting knowledge based on combining heterogeneous information sources is an excellent reference for professionals in signal and image processing, machine learning, data and sensor fusion, computational intelligence, knowledge discovery, pattern recognition, and environmental science and engineering.
Mass communications --- Information systems --- Computer. Automation --- beeldverwerking --- signal processing --- database management --- communicatietechnologie --- signaalverwerking
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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.
Information technology industries --- open contours --- similarly shaped fish species --- Discrete Cosine Transform (DCT) --- Discrete Fourier Transform (DFT) --- Extreme Learning Machines (ELM) --- feature engineering --- small data-sets --- optimization --- machine learning --- preprocessing --- image generation --- weighted interpolation map --- binarization --- single sample per person --- root canal measurement --- multifrequency impedance --- data augmentation --- neural network --- functional magnetic resonance imaging --- independent component analysis --- deep learning --- recurrent neural network --- functional connectivity --- episodic memory --- small sample learning --- feature selection --- noise elimination --- space consistency --- label correlations --- empirical mode decomposition --- sparse representations --- tensor decomposition --- tensor completion --- machine translation --- pairwise evaluation --- educational data --- small datasets --- noisy datasets --- smart building --- Internet of Things (IoT) --- Markov Chain Monte Carlo (MCMC) --- ontology --- graph model --- Artificial Neural Network --- Discriminant Analysis --- dengue --- feature extraction --- sound event detection --- non-negative matrix factorization --- ultrasound images --- shadow detection --- shadow estimation --- auto-encoders --- semi-supervised learning --- prediction --- feature importance --- feature elimination --- hierarchical clustering --- Parkinson’s disease --- few-shot learning --- permutation-variable importance --- topological data analysis --- persistent entropy --- support-vector machine --- data science --- intelligent decision support --- social vulnerability --- gender-gap --- digital-gap --- COVID19 --- policy-making support --- artificial intelligence --- imperfect dataset
Choose an application
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.
open contours --- similarly shaped fish species --- Discrete Cosine Transform (DCT) --- Discrete Fourier Transform (DFT) --- Extreme Learning Machines (ELM) --- feature engineering --- small data-sets --- optimization --- machine learning --- preprocessing --- image generation --- weighted interpolation map --- binarization --- single sample per person --- root canal measurement --- multifrequency impedance --- data augmentation --- neural network --- functional magnetic resonance imaging --- independent component analysis --- deep learning --- recurrent neural network --- functional connectivity --- episodic memory --- small sample learning --- feature selection --- noise elimination --- space consistency --- label correlations --- empirical mode decomposition --- sparse representations --- tensor decomposition --- tensor completion --- machine translation --- pairwise evaluation --- educational data --- small datasets --- noisy datasets --- smart building --- Internet of Things (IoT) --- Markov Chain Monte Carlo (MCMC) --- ontology --- graph model --- Artificial Neural Network --- Discriminant Analysis --- dengue --- feature extraction --- sound event detection --- non-negative matrix factorization --- ultrasound images --- shadow detection --- shadow estimation --- auto-encoders --- semi-supervised learning --- prediction --- feature importance --- feature elimination --- hierarchical clustering --- Parkinson’s disease --- few-shot learning --- permutation-variable importance --- topological data analysis --- persistent entropy --- support-vector machine --- data science --- intelligent decision support --- social vulnerability --- gender-gap --- digital-gap --- COVID19 --- policy-making support --- artificial intelligence --- imperfect dataset
Choose an application
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.
Information technology industries --- open contours --- similarly shaped fish species --- Discrete Cosine Transform (DCT) --- Discrete Fourier Transform (DFT) --- Extreme Learning Machines (ELM) --- feature engineering --- small data-sets --- optimization --- machine learning --- preprocessing --- image generation --- weighted interpolation map --- binarization --- single sample per person --- root canal measurement --- multifrequency impedance --- data augmentation --- neural network --- functional magnetic resonance imaging --- independent component analysis --- deep learning --- recurrent neural network --- functional connectivity --- episodic memory --- small sample learning --- feature selection --- noise elimination --- space consistency --- label correlations --- empirical mode decomposition --- sparse representations --- tensor decomposition --- tensor completion --- machine translation --- pairwise evaluation --- educational data --- small datasets --- noisy datasets --- smart building --- Internet of Things (IoT) --- Markov Chain Monte Carlo (MCMC) --- ontology --- graph model --- Artificial Neural Network --- Discriminant Analysis --- dengue --- feature extraction --- sound event detection --- non-negative matrix factorization --- ultrasound images --- shadow detection --- shadow estimation --- auto-encoders --- semi-supervised learning --- prediction --- feature importance --- feature elimination --- hierarchical clustering --- Parkinson’s disease --- few-shot learning --- permutation-variable importance --- topological data analysis --- persistent entropy --- support-vector machine --- data science --- intelligent decision support --- social vulnerability --- gender-gap --- digital-gap --- COVID19 --- policy-making support --- artificial intelligence --- imperfect dataset --- open contours --- similarly shaped fish species --- Discrete Cosine Transform (DCT) --- Discrete Fourier Transform (DFT) --- Extreme Learning Machines (ELM) --- feature engineering --- small data-sets --- optimization --- machine learning --- preprocessing --- image generation --- weighted interpolation map --- binarization --- single sample per person --- root canal measurement --- multifrequency impedance --- data augmentation --- neural network --- functional magnetic resonance imaging --- independent component analysis --- deep learning --- recurrent neural network --- functional connectivity --- episodic memory --- small sample learning --- feature selection --- noise elimination --- space consistency --- label correlations --- empirical mode decomposition --- sparse representations --- tensor decomposition --- tensor completion --- machine translation --- pairwise evaluation --- educational data --- small datasets --- noisy datasets --- smart building --- Internet of Things (IoT) --- Markov Chain Monte Carlo (MCMC) --- ontology --- graph model --- Artificial Neural Network --- Discriminant Analysis --- dengue --- feature extraction --- sound event detection --- non-negative matrix factorization --- ultrasound images --- shadow detection --- shadow estimation --- auto-encoders --- semi-supervised learning --- prediction --- feature importance --- feature elimination --- hierarchical clustering --- Parkinson’s disease --- few-shot learning --- permutation-variable importance --- topological data analysis --- persistent entropy --- support-vector machine --- data science --- intelligent decision support --- social vulnerability --- gender-gap --- digital-gap --- COVID19 --- policy-making support --- artificial intelligence --- imperfect dataset
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