Geschäftskunden Kundenprogramme
Vor Ort
Merkzettel Warenkorb
  • Produktbild: Artificial Neural Networks in Pattern Recognition
  • Produktbild: Artificial Neural Networks in Pattern Recognition
Band 15154

Artificial Neural Networks in Pattern Recognition 11th IAPR TC3 Workshop, ANNPR 2024, Montreal, QC, Canada, October 10–12, 2024, Proceedings

Aus der Reihe Lecture Notes in Computer Science

Fr. 106.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.09.2024

Abbildungen

XI, 334 p. 100 illus., 81 illus. in color.

Herausgeber

Ching Yee Suen + weitere

Verlag

Springer

Seitenzahl

334

Maße (L/B/H)

23.5/15.5/1.9 cm

Gewicht

528 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-71601-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

19.09.2024

Abbildungen

XI, 334 p. 100 illus., 81 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

334

Maße (L/B/H)

23.5/15.5/1.9 cm

Gewicht

528 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-71601-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Artificial Neural Networks in Pattern Recognition
  • Produktbild: Artificial Neural Networks in Pattern Recognition

  • .- Learning Algorithms and Architectures.


    .- Learning Graph Matching with Graph Neural Networks.


    .- Gaussian-mixture Neural Networks.


    .- Neural Decompiling of Tracr Transformers.


    .- Pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data.


    .- Robust Clustering with McDonald’s Beta-Liouville Mixture Models for Proportional Data.


    .- Evaluating Support Vector Machines with Multiple Kernels by Random Search.


    .- Applications in Medical and Health Sciences.


    .- Automatic Interpretation of 18F-fluorocholine PET/CT Findings in Patients With Primary Hyperparathyroidism: A Novel Dataset with Benchmarks.


    .- A Hybrid Neuroevolutionary Approach to the Design of Convolutional Neural Networks for 2D and 3D Medical Image Segmentation.


    .- An Improved Pix2Pix GAN for Medical Image Generation.


    .- Vision Transformer Features-based Leukemia Classification.


    .- Comparative Study of Deep Learning Models in Melanoma Detection.


    .- A Metaheuristic Optimization Based Deep Feature Selection for Oral Cancer Classification.


    .- Machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson’s Disease.


    .- Explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task.


    .- Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networks.


    .- Applications in Computer Vision.


    .- VAeViT: Fusing Multi-Views for Complete 3D Object Recognition.


    .- Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos.


    .- Palmprint Classification via Filter Faces and Feature Extraction.


    .- Deep Multi-Label Classification of Personality with Handwriting Analysis.


    .- License Plate Detection and Character Recognition Using Deep Learning and Font Evaluation.


    .- Applications in NLP, Speech, and Music.


    .- Experiments in Modeling Disagreement.


    .- Deep Multiresolution Wavelet Transform for Speech Emotion Assessment of High-Risk Suicide Callers.


    .- Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming.


    .- Applications in Environmental and Biological Sciences.


    .- Leveraging LSTM Embeddings for River Water Temperature Modeling.


    .- Research on the Identification of Common Economic Shellfish in Jiangsu Based on Fused-ResNet Network.


    .- Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions.


    .- A Simulation Study on Energy Optimization in Building Control with



    Reinforcement Learning.