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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
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| 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 |
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Aggarwal Charu C.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 | ||
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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 | ||
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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 |
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Nwanganga Fred Chukwuka
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| Indianapolis : , : John Wiley and Sons, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Nwanganga Fred Chukwuka
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| Indianapolis : , : John Wiley and Sons, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Nwanganga Fred Chukwuka
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| Indianapolis : , : John Wiley and Sons, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Unpingco José <1969->
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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 |
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Unpingco José <1969->
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Lavrač Nada
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| Cham, Switzerland : , : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Soggetto topico
-
Aprenentatge automàtic
(101)
- Machine learning (64)
- Machine Learning (17)
- Processament de dades (17)
- Intel·ligència artificial (15)