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Evolutionary Computation & Swarm Intelligence
| Evolutionary Computation & Swarm Intelligence |
| Autore | Caraffini Fabio |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (286 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
dynamic stream clustering
online clustering metaheuristics optimisation population based algorithms density based clustering k-means centroid concept drift concept evolution imbalanced data screening criteria DE-MPFSC algorithm Markov process entanglement degree data integration PSO robot manipulator analysis kinematic parameters identification approximate matching context-triggered piecewise hashing edit distance fuzzy hashing LZJD multi-thread programming sdhash signatures similarity detection ssdeep maximum k-coverage redundant representation normalization genetic algorithm hybrid algorithms memetic algorithms particle swarm multi-objective deterministic optimization, derivative-free global/local optimization simulation-based design optimization wireless sensor networks routing Swarm Intelligence Particle Swarm Optimization Social Network Optimization compact optimization discrete optimization large-scale optimization one billion variables evolutionary algorithms estimation distribution algorithms algorithmic design metaheuristic optimisation evolutionary computation swarm intelligence memetic computing parameter tuning fitness trend Wilcoxon rank-sum Holm–Bonferroni benchmark suite data sampling feature selection instance weighting nature-inspired algorithms meta-heuristic algorithms |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557283803321 |
Caraffini Fabio
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Statistical Machine Learning for Human Behaviour Analysis
| Statistical Machine Learning for Human Behaviour Analysis |
| Autore | Moeslund Thomas |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (300 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
multi-objective evolutionary algorithms
rule-based classifiers interpretable machine learning categorical data hand sign language deep learning restricted Boltzmann machine (RBM) multi-modal profoundly deaf noisy image ensemble methods adaptive classifiers recurrent concepts concept drift stock price direction prediction toe-off detection gait event silhouettes difference convolutional neural network saliency detection foggy image spatial domain frequency domain object contour detection discrete stationary wavelet transform attention allocation attention behavior hybrid entropy information entropy single pixel single photon image acquisition time-of-flight action recognition fibromyalgia Learning Using Concave and Convex Kernels Empatica E4 self-reported survey speech emotion recognition 3D convolutional neural networks k-means clustering spectrograms context-aware framework accuracy false negative rate individual behavior estimation statistical-based time-frequency domain and crowd condition emotion recognition gestures body movements Kinect sensor neural networks face analysis face segmentation head pose estimation age classification gender classification singular point detection boundary segmentation blurring detection fingerprint image enhancement fingerprint quality speech committee of classifiers biometric recognition multimodal-based human identification privacy privacy-aware |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557288403321 |
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Moeslund Thomas
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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