Progress in Computer Recognition Systems. (2019)

• Record Type: Book
• Further Information: Note: Robert Burduk, Marek Kurzynski, Michał Wozniak, editors.
• Other Names: Burduk, Robert; Kurzyński, Marek; Woźniak, Michał
• Contents: Intro; Preface; Contents; Graph Grammar Models in Syntactic Pattern Recognition; 1 Introduction; 2 Graph Grammars in Syntactic Pattern Recognition; 3 Computational Complexity of Graph Parsers; 4 Graph Parsing Schemes in Syntactic Pattern Recognition; 5 Concluding Remarks; References; Computer Vision Methods for Non-destructive Quality Assessment in Additive Manufacturing; 1 Introduction; 2 Vision Based Monitoring of 3D Printing Process; 3 Quality Assessment of 3D Printed Surfaces; 3.1 Visual Feedback for Quality Monitoring; 3.2 Description of Experimental Setup 3.3 Overview of Investigated Approaches3.4 Discussion of the Most Essential Results; 4 Concluding Remarks; References; Combined kNN Classifier for Classification of Incomplete Data; 1 Introduction; 2 Method; 3 Experiment; 4 Results; 5 Conclusions; References; Object Detection in Design Diagrams with Machine Learning; 1 Introduction; 2 Related Works; 3 Yolo for Object Detection in Engineering Drawings; 3.1 Artificial Apros Experiments; 3.2 Experiments with Real Pöyry Data; 4 Learnings; 5 Conclusions; References; Separation of Speech from Speech Interference Based on EGG; 1 Introduction 2 Related Works3 The Proposed System; 3.1 SUV Separation from Mixture; 3.2 Voiced Speech Separation; 3.3 Unvoiced Speech Separation; 3.4 SUV Re-synthesis; 4 Dataset; 5 Experiments and Results; 5.1 Objective Evaluation Criteria; 5.2 Subjective Evaluation Measures; 6 Conclusion; References; Improving the Quality of Clustering-Based Diagnostic Rules by Lowering Dimension of the Cluster Prototypes; 1 Introduction; 2 Rule-Based Diagnosis Support; 3 Prototype-Based Rule Premise Conditions; 4 Experiments; 5 Discussion and Conclusions; References Exploiting Label Interdependencies in Multi-label Classification1 Introduction; 2 Related Work; 3 Materials and Methods; 3.1 Proposed Approach; 3.2 Evaluation; 4 Experiments and Discussion; 5 Conclusions; References; Toward Shareable Multi-abstraction-level Feature Extractor Based on a Bayesian Network; 1 Introduction; 1.1 Motivation; 1.2 Shareable Feature Extractor; 1.3 Related Work; 2 Multi-abstraction-level Feature Extractor Using a Bayesian Network (MFB); 2.1 Parameter Reduction; 2.2 Invariance of Pooling by Child; 3 Experimental Results; 3.1 Pattern Extraction Experiments 3.2 Pooling Layer Training Experiments4 Discussion; 4.1 Bidirectional Recognition of Multiple Abstraction Features; 4.2 Pooling Layer Training; 4.3 Disadvantages of Bidirectional Recognition; 4.4 Conclusion; References; Factorization Machines for Blog Feedback Prediction; 1 Introduction; 2 Factorization Machines; 3 Experimental Evaluation; 4 Conclusions and Outlook; References; Vertical and Horizontal Data Partitioning for Classifier Ensemble Learning; 1 Introduction; 2 Ensemble Classification; 2.1 Generating the Base Experts; 2.2 Predictions of Experts; 2.3 Ensemble Pruning
• Publisher Details: Cham : Springer
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 621.382/2; Signal processing -- Digital techniques; Pattern recognition systems; TECHNOLOGY & ENGINEERING / Mechanical; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030197384; 3030197387
• Related ISBNs: 9783030197377
• Notes: Note: Online resource; title from PDF file page (EBSCO, viewed May 9, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.