001452145 000__ 05813cam\a2200625\i\4500
001452145 001__ 1452145
001452145 003__ OCoLC
001452145 005__ 20230310003342.0
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001452145 007__ cr\un\nnnunnun
001452145 008__ 230111s2022\\\\sz\a\\\\o\\\\\101\0\eng\d
001452145 019__ $$a1356798332$$a1357017844
001452145 020__ $$a9783031230288$$q(electronic bk.)
001452145 020__ $$a3031230280$$q(electronic bk.)
001452145 020__ $$z9783031230271$$q(print)
001452145 020__ $$z3031230272
001452145 0247_ $$a10.1007/978-3-031-23028-8$$2doi
001452145 035__ $$aSP(OCoLC)1357548054
001452145 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCQ$$dBRX
001452145 049__ $$aISEA
001452145 050_4 $$aTK7882.P3
001452145 08204 $$a006.4$$223/eng/20230111
001452145 1112_ $$aInternational Workshop on Structural and Syntactic Pattern Recognition$$d(2022 :$$cMontréal, Québec)
001452145 24510 $$aStructural, syntactic, and statistical pattern recognition :$$bjoint IAPR international workshops, S+SSPR 2022, Montreal, QC, Canada, August 26-27, 2022, Proceedings /$$cAdam Krzyzak, Ching Y. Suen, Andrea Torsello, Nicola Nobile (eds.).
001452145 2463_ $$aS+SSPR 2022
001452145 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001452145 300__ $$a1 online resource (xiii, 324 pages) :$$billustrations (some color).
001452145 336__ $$atext$$btxt$$2rdacontent
001452145 337__ $$acomputer$$bc$$2rdamedia
001452145 338__ $$aonline resource$$bcr$$2rdacarrier
001452145 4901_ $$aLecture Notes in Computer Science,$$x1611-3349 ;$$v13813
001452145 500__ $$aIncludes author index.
001452145 5050_ $$aIntro -- Preface -- Organization -- Contents -- Realization of Autoencoders by Kernel Methods -- 1 Introduction -- 2 Related Work -- 3 Autoencoders by Kernel Methods -- 3.1 Encoder and Decoder -- 3.2 Fundamental Mapping Without Loss -- 3.3 Kernelized Autoencoder -- 4 Comparison with Neural Networks -- 5 Applications -- 5.1 Denoising Autoencoders -- 5.2 Generative Autoencoders -- 6 Discussion -- 7 Conclusion -- References -- Maximal Independent Vertex Set Applied to Graph Pooling -- 1 Introduction -- 2 Related Work -- 2.1 Graph Pooling -- 3 Proposed Method
001452145 5058_ $$a3.1 Maximal Independent Vertex Set (MIVS) -- 3.2 Adaptation of MIVS to Deep Learning -- 4 Experiments -- 4.1 Datasets -- 4.2 Model Architecture and Training Procedure -- 4.3 Ablation Studies -- 4.4 Comparison of MIVSPool According to Other Methods -- 5 Conclusion -- References -- Annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching -- 1 Introduction -- 2 Kieu Database -- 3 Annotation-Free Keyword Spotting (KWS) -- 3.1 Synthetic Dataset Creation -- 3.2 Character Detection -- 3.3 Graph Extraction -- 3.4 Graph Matching -- 3.5 Keyword Spotting (KWS)
001452145 5058_ $$a4 Experimental Evaluation -- 4.1 Task Setup and Parameter Optimization -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusions -- References -- Interactive Generalized Dirichlet Mixture Allocation Model -- 1 Introduction -- 2 Model Description -- 3 Variational Inference -- 4 Interactive Learning Algorithm -- 5 Experimental Results -- 6 Conclusion -- References -- Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment -- 1 Introduction -- 2 Related Work -- 3 Features Soft-Alignment Graph Neural Networks -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Ablation Study
001452145 5058_ $$a4.3 Graph Classification Results -- 4.4 Graph Regression Results -- 5 Conclusion -- References -- Review of Handwriting Analysis for Predicting Personality Traits -- 1 Introduction -- 1.1 History -- 1.2 Applications -- 1.3 Requirements -- 2 Research Progress -- 2.1 Advantages -- 2.2 Disadvantages -- 3 Research Steps -- 3.1 Database -- 3.2 Pre-processing -- 3.3 Feature Extraction -- 3.4 Personality Trait -- 3.5 Prediction Model -- 3.6 Performance Measurement -- 4 Experiment and Future Work -- 4.1 Experiment -- 4.2 Future Work -- References
001452145 5058_ $$aGraph Reduction Neural Networks for Structural Pattern Recognition -- 1 Introduction and Related Work -- 2 Graph Matching on GNN Reduced Graphs -- 2.1 Graph Reduction Neural Network (GReNN) -- 2.2 Classification of GReNN Reduced Graphs -- 3 Empirical Evaluations -- 3.1 Datasets and Experimental Setup -- 3.2 Analysis of the Structure of the Reduced Graphs -- 3.3 Classification Results -- 3.4 Ablation Study -- 4 Conclusions and Future Work -- References -- Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach -- 1 Introduction -- 2 Related Work
001452145 506__ $$aAccess limited to authorized users.
001452145 520__ $$aThis book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022. The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and pattern recognition and much more.
001452145 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 11, 2023).
001452145 650_0 $$aPattern recognition systems$$vCongresses.
001452145 650_0 $$aComputer vision$$vCongresses.
001452145 655_0 $$aElectronic books.
001452145 7001_ $$aKrzyzak, Adam,$$eeditor.$$1https://orcid.org/0000-0003-0766-2659
001452145 7001_ $$aSuen, Ching Y.,$$eeditor.
001452145 7001_ $$aTorsello, Andrea,$$eeditor.
001452145 7001_ $$aNobile, Nicola,$$eeditor.$$1https://orcid.org/0000-0002-1459-9048
001452145 77608 $$iPrint version: $$z3031230272$$z9783031230271$$w(OCoLC)1351463359
001452145 830_0 $$aLecture notes in computer science ;$$v13813.$$x1611-3349
001452145 852__ $$bebk
001452145 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-23028-8$$zOnline Access$$91397441.1
001452145 909CO $$ooai:library.usi.edu:1452145$$pGLOBAL_SET
001452145 980__ $$aBIB
001452145 980__ $$aEBOOK
001452145 982__ $$aEbook
001452145 983__ $$aOnline
001452145 994__ $$a92$$bISE