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Multimodal Affective Computing : Technologies and Applications in Learning Environments / / by Ramón Zatarain Cabada, Héctor Manuel Cárdenas López, Hugo Jair Escalante
Multimodal Affective Computing : Technologies and Applications in Learning Environments / / by Ramón Zatarain Cabada, Héctor Manuel Cárdenas López, Hugo Jair Escalante
Autore Cabada Ramón Zatarain
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (211 pages)
Disciplina 371.334
Altri autori (Persone) LópezHéctor Manuel Cárdenas
EscalanteHugo Jair
Soggetto topico Machine learning
Learning, Psychology of
Pattern recognition systems
User interfaces (Computer systems)
Human-computer interaction
Machine Learning
Learning Theory
Automated Pattern Recognition
User Interfaces and Human Computer Interaction
Aprenentatge automàtic
Interacció persona-ordinador
Interfícies d'usuari (Sistemes d'ordinadors)
Soggetto genere / forma Llibres electrònics
ISBN 3-031-32542-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I: Fundamentals -- Chapter 1. Affective Computing -- Chapter 2. Machine learning and pattern recognition in affective computing -- Chapter 3. Affective Learning Environments -- Part II: Sentiment Analysis for Learning Environments -- Chapter 4. Building resources for sentiment detection -- Chapter 5. Methods for data representation -- Chapter 6. Designing and testing the classification models -- Chapter 7. Model integration to a learning system -- Part III: Multimodal Recognition of Learning-Oriented Emotions -- Chapter 8. Building Resources for Emotion Detection -- Chapter 9. Methods for Data Representation -- Chapter 10. Multimodal recognition systems -- Chapter 11. Multimodal emotion recognition in learning environments -- Part IV: Automatic Personality Recognition -- Chapter 12. Building resources for personality recognition -- Chapter 13. Methods for data representation -- Chapter 14. Personality recognition models -- Chapter 15. Multimodal personality recognition for affective computing.
Record Nr. UNINA-9910734829703321
Cabada Ramón Zatarain  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Neural Networks and Deep Learning : A Textbook / / by Charu C. Aggarwal
Neural Networks and Deep Learning : A Textbook / / by Charu C. Aggarwal
Autore Aggarwal Charu C.
Edizione [2nd ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (xxiv, 529 pages) : illustrations
Disciplina 006.32
Soggetto topico Machine learning
Data mining
Artificial intelligence
Expert systems (Computer science)
Natural language processing (Computer science)
Machine Learning
Data Mining and Knowledge Discovery
Artificial Intelligence
Knowledge Based Systems
Natural Language Processing (NLP)
Xarxes neuronals (Informàtica)
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 3-031-29642-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto An Introduction to Neural Networks -- The Backpropagation Algorithm -- Machine Learning with Shallow Neural Networks -- Deep Learning: Principles and Training Algorithms -- Teaching a Deep Neural Network to Generalize -- Radial Basis Function Networks -- Restricted Boltzmann Machines -- Recurrent Neural Networks -- Convolutional Neural Networks -- Graph Neural Networks -- Deep Reinforcement Learning -- Advanced Topics in Deep Learning.
Record Nr. UNINA-9910734836503321
Aggarwal Charu C.  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Optimization and Data Science: Trends and Applications [[electronic resource] ] : 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event / / edited by Adriano Masone, Veronica Dal Sasso, Valentina Morandi
Optimization and Data Science: Trends and Applications [[electronic resource] ] : 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event / / edited by Adriano Masone, Veronica Dal Sasso, Valentina Morandi
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (189 pages)
Disciplina 519.6
Collana AIRO Springer Series
Soggetto topico Operations research
Management science
Computer science—Mathematics
Discrete mathematics
Operations Research, Management Science
Discrete Mathematics in Computer Science
Optimització matemàtica
Aprenentatge automàtic
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-86286-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466561003316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Optimization and Data Science: Trends and Applications : 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event / / edited by Adriano Masone, Veronica Dal Sasso, Valentina Morandi
Optimization and Data Science: Trends and Applications : 5th AIROYoung Workshop and AIRO PhD School 2021 Joint Event / / edited by Adriano Masone, Veronica Dal Sasso, Valentina Morandi
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (189 pages)
Disciplina 519.6
Collana AIRO Springer Series
Soggetto topico Operations research
Management science
Computer science—Mathematics
Discrete mathematics
Operations Research, Management Science
Discrete Mathematics in Computer Science
Optimització matemàtica
Aprenentatge automàtic
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-86286-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910767533403321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Practical machine learning in R / / Fred Nwanganga, Mike Chapple
Practical machine learning in R / / Fred Nwanganga, Mike Chapple
Autore Nwanganga Fred Chukwuka
Pubbl/distr/stampa Indianapolis : , : John Wiley and Sons, , [2020]
Descrizione fisica 1 online resource (466 pages) : illustrations
Disciplina 617.9
Soggetto topico Machine learning
R (Computer program language)
Aprenentatge automàtic
R (Llenguatge de programació)
Soggetto genere / forma Electronic books.
Llibres electrònics
ISBN 1-5231-3319-8
1-119-59157-0
1-119-59153-8
1-119-59154-6
9781119591535
1119591538
9781119591573
1119591570
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910554806603321
Nwanganga Fred Chukwuka  
Indianapolis : , : John Wiley and Sons, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Practical machine learning in R / / Fred Nwanganga, Mike Chapple
Practical machine learning in R / / Fred Nwanganga, Mike Chapple
Autore Nwanganga Fred Chukwuka
Pubbl/distr/stampa Indianapolis : , : John Wiley and Sons, , [2020]
Descrizione fisica 1 online resource (466 pages) : illustrations
Disciplina 617.9
Soggetto topico Machine learning
R (Computer program language)
Aprenentatge automàtic
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 1-5231-3319-8
1-119-59157-0
1-119-59153-8
1-119-59154-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910677891003321
Nwanganga Fred Chukwuka  
Indianapolis : , : John Wiley and Sons, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Practical machine learning in R / / Fred Nwanganga, Mike Chapple
Practical machine learning in R / / Fred Nwanganga, Mike Chapple
Autore Nwanganga Fred Chukwuka
Pubbl/distr/stampa Indianapolis : , : John Wiley and Sons, , [2020]
Descrizione fisica 1 online resource (466 pages) : illustrations
Disciplina 617.9
Soggetto topico Machine learning
R (Computer program language)
Aprenentatge automàtic
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 1-5231-3319-8
1-119-59157-0
1-119-59153-8
1-119-59154-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910815757803321
Nwanganga Fred Chukwuka  
Indianapolis : , : John Wiley and Sons, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Python for Probability, Statistics, and Machine Learning [[electronic resource] /] / by José Unpingco
Python for Probability, Statistics, and Machine Learning [[electronic resource] /] / by José Unpingco
Autore Unpingco José <1969->
Edizione [3rd ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (524 pages)
Disciplina 006.31
Soggetto topico Telecommunication
Computer science - Mathematics
Mathematical statistics
Engineering mathematics
Engineering - Data processing
Statistics
Data mining
Communications Engineering, Networks
Probability and Statistics in Computer Science
Mathematical and Computational Engineering Applications
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Data Mining and Knowledge Discovery
Python (Llenguatge de programació)
Aprenentatge automàtic
Probabilitats
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 9783031046483
9783031046476
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Part 1 Getting Started with Scientific Python -- Installation and Setup -- Numpy -- Matplotlib -- Ipython -- Jupyter Notebook -- Scipy -- Pandas -- Sympy -- Interfacing with Compiled Libraries -- Integrated Development Environments -- Quick Guide to Performance and Parallel Programming -- Other Resources -- Part 2 Probability -- Introduction -- Projection Methods -- Conditional Expectation as Projection -- Conditional Expectation and Mean Squared Error -- Worked Examples of Conditional Expectation and Mean Square Error Optimization -- Useful Distributions -- Information Entropy -- Moment Generating Functions -- Monte Carlo Sampling Methods -- Useful Inequalities -- Part 3 Statistics -- Python Modules for Statistics -- Types of Convergence -- Estimation Using Maximum Likelihood -- Hypothesis Testing and P-Values -- Confidence Intervals -- Linear Regression -- Maximum A-Posteriori -- Robust Statistics -- Bootstrapping -- Gauss Markov -- Nonparametric Methods -- Survival Analysis -- Part 4 Machine Learning -- Introduction -- Python Machine Learning Modules -- Theory of Learning -- Decision Trees -- Boosting Trees -- Logistic Regression -- Generalized Linear Models -- Regularization -- Support Vector Machines -- Dimensionality Reduction -- Clustering -- Ensemble Methods -- Deep Learning -- Notation -- References -- Index.
Record Nr. UNISA-996499872203316
Unpingco José <1969->  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Python for Probability, Statistics, and Machine Learning / / by José Unpingco
Python for Probability, Statistics, and Machine Learning / / by José Unpingco
Autore Unpingco José <1969->
Edizione [3rd ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (524 pages)
Disciplina 006.31
005.133
Soggetto topico Telecommunication
Computer science - Mathematics
Mathematical statistics
Engineering mathematics
Engineering - Data processing
Statistics
Data mining
Communications Engineering, Networks
Probability and Statistics in Computer Science
Mathematical and Computational Engineering Applications
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Data Mining and Knowledge Discovery
Python (Llenguatge de programació)
Aprenentatge automàtic
Probabilitats
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 9783031046483
9783031046476
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Part 1 Getting Started with Scientific Python -- Installation and Setup -- Numpy -- Matplotlib -- Ipython -- Jupyter Notebook -- Scipy -- Pandas -- Sympy -- Interfacing with Compiled Libraries -- Integrated Development Environments -- Quick Guide to Performance and Parallel Programming -- Other Resources -- Part 2 Probability -- Introduction -- Projection Methods -- Conditional Expectation as Projection -- Conditional Expectation and Mean Squared Error -- Worked Examples of Conditional Expectation and Mean Square Error Optimization -- Useful Distributions -- Information Entropy -- Moment Generating Functions -- Monte Carlo Sampling Methods -- Useful Inequalities -- Part 3 Statistics -- Python Modules for Statistics -- Types of Convergence -- Estimation Using Maximum Likelihood -- Hypothesis Testing and P-Values -- Confidence Intervals -- Linear Regression -- Maximum A-Posteriori -- Robust Statistics -- Bootstrapping -- Gauss Markov -- Nonparametric Methods -- Survival Analysis -- Part 4 Machine Learning -- Introduction -- Python Machine Learning Modules -- Theory of Learning -- Decision Trees -- Boosting Trees -- Logistic Regression -- Generalized Linear Models -- Regularization -- Support Vector Machines -- Dimensionality Reduction -- Clustering -- Ensemble Methods -- Deep Learning -- Notation -- References -- Index.
Record Nr. UNINA-9910629298103321
Unpingco José <1969->  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Representation learning : propositionalization and embeddings / / Nada Lavrac, Vid Podpecan, Marko Robnik-Sikonja
Representation learning : propositionalization and embeddings / / Nada Lavrac, Vid Podpecan, Marko Robnik-Sikonja
Autore Lavrač Nada
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , 2021
Descrizione fisica 1 online resource (175 pages)
Disciplina 006.31
Soggetto topico Machine learning
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 3-030-68817-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Contents -- 1 Introduction to Representation Learning -- 1.1 Motivation -- 1.2 Representation Learning in Knowledge Discovery -- 1.2.1 Machine Learning and Knowledge Discovery -- 1.2.2 Automated Data Transformation -- 1.3 Data Transformations and Information Representation Levels -- 1.3.1 Information Representation Levels -- 1.3.2 Propositionalization: Learning Symbolic Vector Representations -- 1.3.3 Embeddings: Learning Numeric Vector Representations -- 1.4 Evaluation of Propositionalization and Embeddings -- 1.4.1 Performance Evaluation -- 1.4.2 Interpretability -- 1.5 Survey of Automated Data Transformation Methods -- 1.6 Outline of This Monograph -- References -- 2 Machine Learning Background -- 2.1 Machine Learning -- 2.1.1 Attributes and Features -- 2.1.2 Machine Learning Approaches -- 2.1.3 Decision and Regression Tree Learning -- 2.1.4 Rule Learning -- 2.1.5 Kernel Methods -- 2.1.6 Ensemble Methods -- 2.1.7 Deep Neural Networks -- 2.2 Text Mining -- 2.3 Relational Learning -- 2.4 Network Analysis -- 2.4.1 Selected Homogeneous Network Analysis Tasks -- 2.4.2 Selected Heterogeneous Network Analysis Tasks -- 2.4.3 Semantic Data Mining -- 2.4.4 Network Representation Learning -- 2.5 Evaluation -- 2.5.1 Classifier Evaluation Measures -- 2.5.2 Rule Evaluation Measures -- 2.6 Data Mining and Selected Data Mining Platforms -- 2.6.1 Data Mining -- 2.6.2 Selected Data Mining Platforms -- 2.7 Implementation and Reuse -- References -- 3 Text Embeddings -- 3.1 Background Technologies -- 3.1.1 Transfer Learning -- 3.1.2 Language Models -- 3.2 Word Cooccurrence-Based Embeddings -- 3.2.1 Sparse Word Cooccurrence-Based Embeddings -- 3.2.2 Weighting Schemes -- 3.2.3 Similarity Measures -- 3.2.4 Sparse Matrix Representations of Texts -- 3.2.5 Dense Term-Matrix Based Word Embeddings -- 3.2.6 Dense Topic-Based Embeddings.
3.3 Neural Word Embeddings -- 3.3.1 Word2vec Embeddings -- 3.3.2 GloVe Embeddings -- 3.3.3 Contextual Word Embeddings -- 3.4 Sentence and Document Embeddings -- 3.5 Cross-Lingual Embeddings -- 3.6 Intrinsic Evaluation of Text Embeddings -- 3.7 Implementation and Reuse -- 3.7.1 LSA and LDA -- 3.7.2 word2vec -- 3.7.3 BERT -- References -- 4 Propositionalization of Relational Data -- 4.1 Relational Learning -- 4.2 Relational Data Representation -- 4.2.1 Illustrative Example -- 4.2.2 Example Using a Logical Representation -- 4.2.3 Example Using a Relational Database Representation -- 4.3 Propositionalization -- 4.3.1 Relational Features -- 4.3.2 Automated Construction of Relational Features by RSD -- 4.3.3 Automated Data Transformation and Learning -- 4.4 Selected Propositionalization Approaches -- 4.5 Wordification: Unfolding Relational Data into BoW Vectors -- 4.5.1 Outline of the Wordification Approach -- 4.5.2 Wordification Algorithm -- 4.5.3 Improved Efficiency of Wordification Algorithm -- 4.6 Deep Relational Machines -- 4.7 Implementation and Reuse -- 4.7.1 Wordification -- 4.7.2 Python-rdm Package -- References -- 5 Graph and Heterogeneous Network Transformations -- 5.1 Embedding Simple Graphs -- 5.1.1 DeepWalk Algorithm -- 5.1.2 Node2vec Algorithm -- 5.1.3 Other Random Walk-Based Graph Embedding Algorithms -- 5.2 Embedding Heterogeneous Information Networks -- 5.2.1 Heterogeneous Information Networks -- 5.2.2 Examples of Heterogeneous Information Networks -- 5.2.3 Embedding Feature-Rich Graphs with GCNs -- 5.2.4 Other Heterogeneous Network Embedding Approaches -- 5.3 Propositionalizing Heterogeneous Information Networks -- 5.3.1 TEHmINe Propositionalization of Text-Enriched Networks -- 5.3.1.1 Heterogeneous Network Decomposition -- 5.3.1.2 Feature Vector Construction -- 5.3.1.3 Data Fusion -- 5.3.2 HINMINE Heterogeneous Networks Decomposition.
5.4 Ontology Transformations -- 5.4.1 Ontologies and Semantic Data Mining -- 5.4.2 NetSDM Ontology Reduction Methodology -- 5.4.2.1 Converting Ontology and Examples into Network Format -- 5.4.2.2 Term Significance Calculation -- 5.4.2.3 Network Node Removal -- 5.5 Embedding Knowledge Graphs -- 5.6 Implementation and Reuse -- 5.6.1 Node2vec -- 5.6.2 Metapath2vec -- 5.6.3 HINMINE -- References -- 6 Unified Representation Learning Approaches -- 6.1 Entity Embeddings with StarSpace -- 6.2 Unified Approaches for Relational Data -- 6.2.1 PropStar: Feature-Based Relational Embeddings -- 6.2.2 PropDRM: Instance-Based Relational Embeddings -- 6.2.3 Performance Evaluation of Relational Embeddings -- 6.3 Implementation and Reuse -- 6.3.1 StarSpace -- 6.3.2 PropDRM -- References -- 7 Many Faces of Representation Learning -- 7.1 Unifying Aspects in Terms of Data Representation -- 7.2 Unifying Aspects in Terms of Learning -- 7.3 Unifying Aspects in Terms of Use -- 7.4 Summary and Conclusions -- References -- Index.
Record Nr. UNINA-9910492147403321
Lavrač Nada  
Cham, Switzerland : , : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui