Artificial Neural Networks in Pattern Recognition
Springer International Publishing (Verlag)
978-3-030-58308-8 (ISBN)
The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.
Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search.- Sttructured (De)composable Representations Trained with Neural Networks.- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling.- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins.- Abstract Echo State Networks.- Minimal Complexity Support Vector Machines.- Named Entity Disambiguation at Scale.- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds.- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database.- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario.- A deep learning approach for efficient registration of dual view mammography.- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology.- Applications of Generative Adversarial Networks to Dermatologic Imaging.- Typing Plasmids with Distributed Sequence Representation.- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes.- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools.- A Hybrid Deep Learning Approach For Forecasting Air Temperature.- Using CNNs to optimize numerical simulations in geotechnical engineering.- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification.- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture.- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis.- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool.
| Erscheinungsdatum | 03.09.2020 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XI, 306 p. 205 illus., 114 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 492 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Grafik / Design ► Digitale Bildverarbeitung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Applications • Artificial Intelligence • classification • Clustering • clustering algorithms • Computer Science • conference proceedings • Data Security • Feature Selection • Image Processing • Image Segmentation • Informatics • Learning Algorithms • machine learning • network architecture • Neural networks • Object recognition • pattern recognition • Recurrent Neural Networks • Research • supervised learning • Support Vector Machines (SVM) |
| ISBN-10 | 3-030-58308-2 / 3030583082 |
| ISBN-13 | 978-3-030-58308-8 / 9783030583088 |
| Zustand | Neuware |
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