Machine Learning: A Constraint-Based ApproachMorgan Kaufmann, 20 nov 2017 - 580 pagine Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book. This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included. - Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner - Provides in-depth coverage of unsupervised and semi-supervised learning - Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning - Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex |
Sommario
| 60 | |
3 Linear Threshold Machines | 122 |
4 Kernel Machines | 186 |
5 Deep Architectures | 236 |
6 Learning and Reasoning With Constraints | 340 |
7 Epilogue | 446 |
8 Answers to Exercises | 452 |
C1 Functionals and Variations | 518 |
C2 Basic Notion on Variations | 520 |
C3 EulerLagrange Equations | 523 |
C4 Variational Problems With Subsidiary Conditions | 526 |
| 534 | |
| 552 | |
Back Cover | 561 |
Altre edizioni - Visualizza tutto
Machine Learning: A Constraint-Based Approach Marco Gori,Alessandro Betti,Stefano Melacci Anteprima limitata - 2023 |
Machine Learning: A Constraint-Based Approach Marco Gori,Alessandro Betti,Stefano Melacci Anteprima non disponibile - 2023 |
Parole e frasi comuni
agent algorithm analysis arg max arg min associated assume backpropagation Boolean functions classification clearly complexity computational condition configurations constraints construct convex convolutional corresponding decision defined dimension discussion domain dual space environment error function examples Exercise expressed feature space feedforward finite formulation framework given graph Hence hidden layer hidden units induction input Interestingly interpretation invariance kernel machines Lagrangian Lagrangian multipliers learning algorithms learning tasks Let us consider linear machines linearly-separable loss function machine learning matrix minimization neural network neurons Notice optimization output parameters parsimony patterns perceptron points polynomial predicates principle problem promptly Prove pseudoinverse quadratic random variable realization regression regularization representation retina scheme Section sequence softmax solution structure supervised learning supervised pairs Suppose t-norm tion training set vector vertexes w₁ weights yields Ук Хк