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[null,"1.","Record Nr.",null,"UNINA9910964944503321"]
[null,null,"Autore",null,"Theodoridis Sergios <1951->"]
[null,null,"Titolo",null,"Pattern recognition / / Sergios Theodoridis, Konstantinos Koutroumbas"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam ; ; London, : Elsevier/Academic Press, c2009"]
[null,null,"ISBN",null,"9786612541155"]
[{"$nulls":4},"9781282541153"]
[{"$nulls":4},"1282541153"]
[{"$nulls":4},"9780080949123"]
[{"$nulls":4},"0080949126"]
[null,null,"Edizione",null,"[4th ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (981 p.)"]
[null,null,"Disciplina",null,"006.4"]
[null,null,"Soggetti",null,"Pattern recognition systems"]
[{"$nulls":4},"Pattern perception"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Previous ed.: Amsterdam: Academic, 2003."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Front Cover; Pattern Recognition; Copyright Page; Contents; Preface; Chapter 1 Introduction; 1.1 Is Pattern Recognition Important?; 1.2 Features, Feature Vectors, and Classifiers; 1.3 Supervised, Unsupervised, and Semi-Supervised Learning; 1.4 MATLAB Programs; 1.5 Outline of The Book; Chapter 2 Classifiers Based on Bayes Decision Theory; 2.1 Introduction; 2.2 Bayes Decision Theory; 2.3 Discriminant Functions and Decision Surfaces; 2.4 Bayesian Classification for Normal Distributions; 2.5 Estimation of Unknown Probability Density Functions; 2.6 The Nearest Neighbor Rule; 2.7 Bayesian Networks"]
[{"$nulls":4},"2.8 Problems References; Chapter 3 Linear Classifiers; 3.1 Introduction; 3.2 Linear Discriminant Functions and Decision Hyperplanes; 3.3 The Perceptron Algorithm; 3.4 Least Squares Methods; 3.5 Mean Square Estimation Revisited; 3.6 Logistic Discrimination; 3.7 Support Vector Machines; 3.8 Problems; References; Chapter 4 Nonlinear Classifiers; 4.1 Introduction; 4.2 The XOR Problem; 4.3 The Two-Layer Perceptron; 4.4 Three-Layer Perceptrons; 4.5 Algorithms Based on Exact Classification of the Training Set; 4.6 The Back propagation Algorithm; 4.7 Variations on the Back propagation Theme"]
[{"$nulls":4},"4.8 The Cost Function Choice 4.9 Choice of the Network Size; 4.10 A Simulation Example; 4.11 Networks with Weight Sharing; 4.12 Generalized Linear Classifiers; 4.13 Capacity of the l-Dimensional Space in Linear Dichotomies; 4.14 Polynomial Classifiers; 4.15 Radial "]
[{"$nulls":4},"Basis Function Networks; 4.16 Universal Approximators; 4.17 Probabilistic Neural Networks; 4.18 Support Vector Machines:The Nonlinear Case; 4.19 Beyond the SVM Paradigm; 4.20 Decision Trees; 4.21 Combining Classifiers; 4.22 The Boosting Approach to Combine Classifiers; 4.23 The Class Imbalance Problem; 4.24 Discussion; 4.25 Problems"]
[{"$nulls":4},"References Chapter 5 Feature Selection; 5.1 Introduction; 5.2 Preprocessing; 5.3 The Peaking Phenomenon; 5.4 Feature Selection Based on Statistical Hypothesis Testing; 5.5 The Receiver Operating Characteristics (ROC) Curve; 5.6 Class Separability Measures; 5.7 Feature Subset Selection; 5.8 Optimal Feature Generation; 5.9 Neural Networks and Feature Generation/Selection; 5.10 A Hint On Generalization Theory; 5.11 The Bayesian Information Criterion; 5.12 Problems; References; Chapter 6 Feature Generation I: Data Transformation and Dimensionality Reduction; 6.1 Introduction"]
[{"$nulls":4},"6.2 Basis Vectors and Images 6.3 The Karhunen-Loève Transform; 6.4 The Singular Value Decomposition; 6.5 Independent Component Analysis; 6.6 Non-negative Matrix Factorization; 6.7 Nonlinear Dimensionality Reduction; 6.8 The Discrete Fourier Transform (DFT); 6.9 The Discrete Cosine and Sine Transforms; 6.10 The Hadamard Transform; 6.11 The Haar Transform; 6.12 The Haar Expansion Revisited; 6.13 Discrete Time Wavelet Transform (DTWT); 6.14 The Multi-resolution Interpretation; 6.15 Wavelet Packets; 6.16 A Look at Two-Dimensional Generalizations; 6.17 Applications; 6.18 Problems; References"]
[{"$nulls":4},"Chapter 7"]
[null,null,null,"Sommario/riassunto","This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.    · Thoroughly developed to include many more worked examples to give grea"]
[null,"2.","Record Nr.",null,"UNINA9910888047703321"]
[null,null,"Autore",null,"Ryan T. MacNeil"]
[null,null,"Titolo",null,"Observing Dark Innovation"]
[null,null,"Pubbl/distr/stampa",null,"Bristol University Press, 2024"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (192 p.) : ill"]
[null,null,"Disciplina",null,"600"]
[null,null,"Soggetti",null,"Science / Global Warming & Climate Change"]
[{"$nulls":4},"Science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Available open access digitally under CC-BY-NC-ND licence.Why does scholarship on innovation fixate on certain classes of technology? Could our research tools and techniques be concealing as much as they reveal?Ryan T. MacNeil shows how the common instrumentalities of innovation research carry neoliberal market biases. He calls for critical scholars to examine how we observe and understand innovation, offering ways forward to deconstruct and reform disciplinary conventions.This book makes a valuable contribution to critical management and science and technology studies by shedding light on the 'dark matter' of innovation. This will be an important resource for scholars and practitioners interested in disruptive ideas about innovation."]