Coherence [[electronic resource] ] : In Signal Processing and Machine Learning / / by David Ramírez, Ignacio Santamaría, Louis Scharf |
Autore | Ramirez David |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
Descrizione fisica | 1 online resource (495 pages) |
Disciplina | 006.31 |
Soggetto topico |
Signal processing
Computer science - Mathematics Mathematical statistics Machine learning Signal, Speech and Image Processing Probability and Statistics in Computer Science Machine Learning Processament de senyals Aprenentatge automàtic |
Soggetto genere / forma | Llibres electrònics |
ISBN | 3-031-13331-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction -- Historical perspective, motivating problems, and preview of what is to come -- Least Squares and related -- Classical correlations and coherence -- Coherence in the multivariate normal (MVN) model -- Classical tests for correlation -- One-channel matched subspace detectors -- Adaptive subspace detectors -- Two channel matched subspace detectors -- Detection of spatially-correlated time series -- Coherence and the detection of cyclostationarity -- Partial coherence for testing causality -- Subspace averaging -- Coherence and performance bounds -- Variations on coherence -- Conclusion. |
Record Nr. | UNINA-9910637713703321 |
Ramirez David
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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Coherence [[electronic resource] ] : In Signal Processing and Machine Learning / / by David Ramírez, Ignacio Santamaría, Louis Scharf |
Autore | Ramirez David |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
Descrizione fisica | 1 online resource (495 pages) |
Disciplina | 006.31 |
Soggetto topico |
Signal processing
Computer science - Mathematics Mathematical statistics Machine learning Signal, Speech and Image Processing Probability and Statistics in Computer Science Machine Learning Processament de senyals Aprenentatge automàtic |
Soggetto genere / forma | Llibres electrònics |
ISBN | 3-031-13331-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction -- Historical perspective, motivating problems, and preview of what is to come -- Least Squares and related -- Classical correlations and coherence -- Coherence in the multivariate normal (MVN) model -- Classical tests for correlation -- One-channel matched subspace detectors -- Adaptive subspace detectors -- Two channel matched subspace detectors -- Detection of spatially-correlated time series -- Coherence and the detection of cyclostationarity -- Partial coherence for testing causality -- Subspace averaging -- Coherence and performance bounds -- Variations on coherence -- Conclusion. |
Record Nr. | UNISA-996503549103316 |
Ramirez David
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Communication Principles for Data Science [[electronic resource] /] / by Changho Suh |
Autore | Suh Changho |
Edizione | [1st ed. 2023.] |
Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
Descrizione fisica | 1 online resource (294 pages) |
Disciplina | 381 |
Collana | Signals and Communication Technology |
Soggetto topico |
Artificial intelligence—Data processing
Digital media Computer science—Mathematics Mathematical statistics Signal processing Data Science Digital and New Media Probability and Statistics in Computer Science Signal, Speech and Image Processing |
ISBN | 981-19-8008-X |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Preface -- Acknowledgements -- Part 1. Communication over the Gaussian channel -- Chapter 1.Overview of the book -- Chapter 2. A statistical model for additive noise channels -- Chapter 3. Additive Gaussian noise model -- Problem Set 1 -- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle -- Chapter 5. Analysis of error probability -- Chapter 6. Multiple bits transmission via pulse amplitude modulation -- Problem Set 2 -- Chapter 7. Multi-shot communication -- Chapter 8. Repetition coding -- Chapter 9: Capacity of the additive white Gaussian noise channel -- Problem Set 3 -- Part 2. Communication over inter-symbol interference (ISI) channels -- Chapter 10. Signal conversion from discrete to continuous time (1/2) -- Chapter 11. Signal conversion from discrete to continuous time (2/2) -- Chapter 12. Optimal receiver architecture -- Problem Set 4 -- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection -- Chapter 14. Optimal receiver in ISI channels: Viterbi algorithm -- Problem Set 5 -- Chapter 15.Orthogonal frequency division multiplexing (1/3) -- Chapter 16. Orthogonal frequency division multiplexing (2/3) -- Chapter 17. Orthogonal frequency division multiplexing (3/3) -- Problem Set 6 -- Part 3.Data science applications -- Chapter 18. Community detection as a communication problem -- Chapter 19. Community detection: ML principle -- Chapter 20. Community detection: An efficient algorithm -- Chapter 21. Community detection: Python implementation -- Problem Set 7 -- Chapter 22.Haplotype phasing as a communication problem -- Chapter 23. Haplotype phasing: ML principle -- Chapter 24: Haplotype phasing: An efficient algorithm. . |
Record Nr. | UNINA-9910731488503321 |
Suh Changho
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
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Lo trovi qui: Univ. Federico II | ||
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Communication Principles for Data Science [[electronic resource] /] / by Changho Suh |
Autore | Suh Changho |
Edizione | [1st ed. 2023.] |
Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
Descrizione fisica | 1 online resource (294 pages) |
Disciplina | 381 |
Collana | Signals and Communication Technology |
Soggetto topico |
Artificial intelligence—Data processing
Digital media Computer science—Mathematics Mathematical statistics Signal processing Data Science Digital and New Media Probability and Statistics in Computer Science Signal, Speech and Image Processing |
ISBN | 981-19-8008-X |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Preface -- Acknowledgements -- Part 1. Communication over the Gaussian channel -- Chapter 1.Overview of the book -- Chapter 2. A statistical model for additive noise channels -- Chapter 3. Additive Gaussian noise model -- Problem Set 1 -- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle -- Chapter 5. Analysis of error probability -- Chapter 6. Multiple bits transmission via pulse amplitude modulation -- Problem Set 2 -- Chapter 7. Multi-shot communication -- Chapter 8. Repetition coding -- Chapter 9: Capacity of the additive white Gaussian noise channel -- Problem Set 3 -- Part 2. Communication over inter-symbol interference (ISI) channels -- Chapter 10. Signal conversion from discrete to continuous time (1/2) -- Chapter 11. Signal conversion from discrete to continuous time (2/2) -- Chapter 12. Optimal receiver architecture -- Problem Set 4 -- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection -- Chapter 14. Optimal receiver in ISI channels: Viterbi algorithm -- Problem Set 5 -- Chapter 15.Orthogonal frequency division multiplexing (1/3) -- Chapter 16. Orthogonal frequency division multiplexing (2/3) -- Chapter 17. Orthogonal frequency division multiplexing (3/3) -- Problem Set 6 -- Part 3.Data science applications -- Chapter 18. Community detection as a communication problem -- Chapter 19. Community detection: ML principle -- Chapter 20. Community detection: An efficient algorithm -- Chapter 21. Community detection: Python implementation -- Problem Set 7 -- Chapter 22.Haplotype phasing as a communication problem -- Chapter 23. Haplotype phasing: ML principle -- Chapter 24: Haplotype phasing: An efficient algorithm. . |
Record Nr. | UNISA-996546820903316 |
Suh Changho
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Composing Fisher Kernels from Deep Neural Models [[electronic resource] ] : A Practitioner's Approach / / by Tayyaba Azim, Sarah Ahmed |
Autore | Azim Tayyaba |
Edizione | [1st ed. 2018.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
Descrizione fisica | 1 online resource (69 pages) |
Disciplina | 515.9 |
Collana | SpringerBriefs in Computer Science |
Soggetto topico |
Pattern recognition
Signal processing Image processing Speech processing systems Information storage and retrieval Mathematical statistics Data structures (Computer science) Artificial intelligence Pattern Recognition Signal, Image and Speech Processing Information Storage and Retrieval Probability and Statistics in Computer Science Data Storage Representation Artificial Intelligence |
ISBN | 3-319-98524-8 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Chapter 1. Kernel Based Learning: A Pragmatic Approach in the Face of New Challenges -- Chapter 2. Fundamentals of Fisher Kernels -- Chapter 3. Training Deep Models and Deriving Fisher Kernels: A Step Wise Approach -- Chapter 4. Large Scale Image Retrieval and Its Challenges -- Chapter 5. Open Source Knowledge Base for Machine Learning Practitioners. |
Record Nr. | UNINA-9910299348303321 |
Azim Tayyaba
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
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Lo trovi qui: Univ. Federico II | ||
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Computational Complexity and Property Testing [[electronic resource] ] : On the Interplay Between Randomness and Computation / / edited by Oded Goldreich |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (391 pages) : illustrations |
Disciplina | 511.352 |
Collana | Theoretical Computer Science and General Issues |
Soggetto topico |
Computer science
Computer engineering Computer networks Computer science—Mathematics Mathematical statistics Logic programming Application software Data structures (Computer science) Information theory Theory of Computation Computer Engineering and Networks Probability and Statistics in Computer Science Logic in AI Computer and Information Systems Applications Data Structures and Information Theory |
ISBN | 3-030-43662-4 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | A Probabilistic Error-Correcting Scheme that Provides Partial Secrecy -- Bridging a Small Gap in the Gap Ampli cation of Assignment Testers -- On (Valiant's) Polynomial-Size Monotone Formula for Majority -- Two Comments on Targeted Canonical Derandomizers -- On the Effect of the Proximity Parameter on Property Testers -- On the Size of Depth-Three Boolean Circuits for Computing Multilinear Functions -- On the Communication Complexity Methodology for Proving Lower Bounds on the Query Complexity of Property Testing -- Super-Perfect Zero-Knowledge Proofs -- On the Relation between the Relative Earth Mover Distance and the Variation Distance (an exposition) -- The Uniform Distribution is Complete with respect to Testing Identity to a Fixed Distribution -- A Note on Tolerant Testing with One-Sided Error -- On Emulating Interactive Proofs with Public Coins -- Reducing Testing Affine Spaces to Testing Linearity of Functions -- Deconstructing 1-Local Expanders -- Worst-case to Average-case Reductions for Subclasses of P -- On the Optimal Analysis of the Collision Probability Tester (an exposition) -- On Constant-Depth Canonical Boolean Circuits for Computing Multilinear Functions -- Constant-Round Interactive Proof Systems for AC0[2] and NC1 -- Flexible Models for Testing Graph Properties -- Pseudo-Mixing Time of Random Walks -- On Constructing Expanders for any Number of Vertices. |
Record Nr. | UNISA-996418218403316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Computational Complexity and Property Testing [[electronic resource] ] : On the Interplay Between Randomness and Computation / / edited by Oded Goldreich |
Edizione | [1st ed. 2020.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
Descrizione fisica | 1 online resource (391 pages) : illustrations |
Disciplina | 511.352 |
Collana | Theoretical Computer Science and General Issues |
Soggetto topico |
Computer science
Computer engineering Computer networks Computer science—Mathematics Mathematical statistics Logic programming Application software Data structures (Computer science) Information theory Theory of Computation Computer Engineering and Networks Probability and Statistics in Computer Science Logic in AI Computer and Information Systems Applications Data Structures and Information Theory |
ISBN | 3-030-43662-4 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | A Probabilistic Error-Correcting Scheme that Provides Partial Secrecy -- Bridging a Small Gap in the Gap Ampli cation of Assignment Testers -- On (Valiant's) Polynomial-Size Monotone Formula for Majority -- Two Comments on Targeted Canonical Derandomizers -- On the Effect of the Proximity Parameter on Property Testers -- On the Size of Depth-Three Boolean Circuits for Computing Multilinear Functions -- On the Communication Complexity Methodology for Proving Lower Bounds on the Query Complexity of Property Testing -- Super-Perfect Zero-Knowledge Proofs -- On the Relation between the Relative Earth Mover Distance and the Variation Distance (an exposition) -- The Uniform Distribution is Complete with respect to Testing Identity to a Fixed Distribution -- A Note on Tolerant Testing with One-Sided Error -- On Emulating Interactive Proofs with Public Coins -- Reducing Testing Affine Spaces to Testing Linearity of Functions -- Deconstructing 1-Local Expanders -- Worst-case to Average-case Reductions for Subclasses of P -- On the Optimal Analysis of the Collision Probability Tester (an exposition) -- On Constant-Depth Canonical Boolean Circuits for Computing Multilinear Functions -- Constant-Round Interactive Proof Systems for AC0[2] and NC1 -- Flexible Models for Testing Graph Properties -- Pseudo-Mixing Time of Random Walks -- On Constructing Expanders for any Number of Vertices. |
Record Nr. | UNINA-9910409674203321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
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Lo trovi qui: Univ. Federico II | ||
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Computational Intelligence in Music, Sound, Art and Design [[electronic resource] ] : 6th International Conference, EvoMUSART 2017, Amsterdam, The Netherlands, April 19–21, 2017, Proceedings / / edited by João Correia, Vic Ciesielski, Antonios Liapis |
Edizione | [1st ed. 2017.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
Descrizione fisica | 1 online resource (X, 371 p. 169 illus.) |
Disciplina | 005.11 |
Collana | Theoretical Computer Science and General Issues |
Soggetto topico |
Algorithms
Data mining Artificial intelligence Computer vision Computer science—Mathematics Mathematical statistics Digital humanities Data Mining and Knowledge Discovery Artificial Intelligence Computer Vision Probability and Statistics in Computer Science Digital Humanities |
ISBN | 3-319-55750-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Algorithmic Songwriting with ALYSIA -- On Symmetry, Aesthetics and Quantifying Symmetrical Complexity -- Towards Polyphony Reconstruction Using Multidimensional Multiple Sequence Alignment -- Melody Retrieval and Classification Using Biologically-Inspired Techniques -- Evolved Aesthetic Analogies to Improve Artistic Experience -- Deep Artificial Composer: A Creative Neural Network Model for Automated Melody Generation -- A Kind of Bio-inspired Learning of mUsic stylE -- Using Autonomous Agents to Improvise Music Compositions in Real-time -- Generating Polyphonic Music Using Tied Parallel Networks -- Mixed-initiative Creative Drawing with webIconoscope -- Clustering Agents for the Evolution of Autonomous Musical Fitness -- EvoFashion: Customising Fashion Through Evolution -- A Swarm Environment for Experimental Performance and Improvisation -- Niche Constructing Drawing Robots -- Automated Shape Design by Grammatical Evolution -- Evolutionary Image Transition Using Random Walks -- Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos -- Assessing Augmented Creativity: Putting a Lovelace Machine for Interactive Title Generation through a Human Creativity Test -- Play It again: Evolved Audio Effects and Synthesizer Programming -- Fashion Design Aid System with Application of Interactive Genetic Algorithms -- Generalization Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples -- Exploring the Exactitudes Portrait Series with Restricted Boltzmann Machines -- Evolving Mondrian-Style Artworks -- Predicting Expressive Bow Controls for Violin and Viola. . |
Record Nr. | UNISA-996466202903316 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
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Lo trovi qui: Univ. di Salerno | ||
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Computational Intelligence in Music, Sound, Art and Design [[electronic resource] ] : 6th International Conference, EvoMUSART 2017, Amsterdam, The Netherlands, April 19–21, 2017, Proceedings / / edited by João Correia, Vic Ciesielski, Antonios Liapis |
Edizione | [1st ed. 2017.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
Descrizione fisica | 1 online resource (X, 371 p. 169 illus.) |
Disciplina | 005.11 |
Collana | Theoretical Computer Science and General Issues |
Soggetto topico |
Algorithms
Data mining Artificial intelligence Computer vision Computer science—Mathematics Mathematical statistics Digital humanities Data Mining and Knowledge Discovery Artificial Intelligence Computer Vision Probability and Statistics in Computer Science Digital Humanities |
ISBN | 3-319-55750-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Algorithmic Songwriting with ALYSIA -- On Symmetry, Aesthetics and Quantifying Symmetrical Complexity -- Towards Polyphony Reconstruction Using Multidimensional Multiple Sequence Alignment -- Melody Retrieval and Classification Using Biologically-Inspired Techniques -- Evolved Aesthetic Analogies to Improve Artistic Experience -- Deep Artificial Composer: A Creative Neural Network Model for Automated Melody Generation -- A Kind of Bio-inspired Learning of mUsic stylE -- Using Autonomous Agents to Improvise Music Compositions in Real-time -- Generating Polyphonic Music Using Tied Parallel Networks -- Mixed-initiative Creative Drawing with webIconoscope -- Clustering Agents for the Evolution of Autonomous Musical Fitness -- EvoFashion: Customising Fashion Through Evolution -- A Swarm Environment for Experimental Performance and Improvisation -- Niche Constructing Drawing Robots -- Automated Shape Design by Grammatical Evolution -- Evolutionary Image Transition Using Random Walks -- Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos -- Assessing Augmented Creativity: Putting a Lovelace Machine for Interactive Title Generation through a Human Creativity Test -- Play It again: Evolved Audio Effects and Synthesizer Programming -- Fashion Design Aid System with Application of Interactive Genetic Algorithms -- Generalization Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples -- Exploring the Exactitudes Portrait Series with Restricted Boltzmann Machines -- Evolving Mondrian-Style Artworks -- Predicting Expressive Bow Controls for Violin and Viola. . |
Record Nr. | UNINA-9910484653603321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
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Lo trovi qui: Univ. Federico II | ||
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Computational Intelligence Methods for Bioinformatics and Biostatistics [[electronic resource] ] : 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, Revised Selected Papers / / edited by Andrea Bracciali, Giulio Caravagna, David Gilbert, Roberto Tagliaferri |
Edizione | [1st ed. 2017.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
Descrizione fisica | 1 online resource (XXII, 249 p. 98 illus.) |
Disciplina | 006.3 |
Collana | Lecture Notes in Bioinformatics |
Soggetto topico |
Bioinformatics
Artificial intelligence Data mining Computers Mathematical statistics Algorithms Computational Biology/Bioinformatics Artificial Intelligence Data Mining and Knowledge Discovery Computation by Abstract Devices Probability and Statistics in Computer Science Algorithm Analysis and Problem Complexity |
ISBN | 3-319-67834-5 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910484491503321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
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Lo trovi qui: Univ. Federico II | ||
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