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Constructive Neural Networks
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Book Synopsis Constructive Neural Networks by : Leonardo Franco
Download or read book Constructive Neural Networks written by Leonardo Franco and published by Springer. This book was released on 2009-11-25 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of invited works that consider constructive methods for neural networks, taken primarily from papers presented at a special th session held during the 18 International Conference on Artificial Neural Networks (ICANN 2008) in September 2008 in Prague, Czech Republic. The book is devoted to constructive neural networks and other incremental learning algorithms that constitute an alternative to the standard method of finding a correct neural architecture by trial-and-error. These algorithms provide an incremental way of building neural networks with reduced topologies for classification problems. Furthermore, these techniques produce not only the multilayer topologies but the value of the connecting synaptic weights that are determined automatically by the constructing algorithm, avoiding the risk of becoming trapped in local minima as might occur when using gradient descent algorithms such as the popular back-propagation. In most cases the convergence of the constructing algorithms is guaranteed by the method used. Constructive methods for building neural networks can potentially create more compact and robust models which are easily implemented in hardware and used for embedded systems. Thus a growing amount of current research in neural networks is oriented towards this important topic. The purpose of this book is to gather together some of the leading investigators and research groups in this growing area, and to provide an overview of the most recent advances in the techniques being developed for constructive neural networks and their applications.
Download or read book Constructive Neural Networks written by and published by . This book was released on 2011-10-14 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Improving Generalization of Constructive Neural Networks Using Ensembles by : Renee ́Stoneking Renner
Download or read book Improving Generalization of Constructive Neural Networks Using Ensembles written by Renee ́Stoneking Renner and published by . This book was released on 1999 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Constructive Neural Networks by : Nicholas K. Treadgold
Download or read book Constructive Neural Networks written by Nicholas K. Treadgold and published by . This book was released on 1999 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Executive Functions and Constructive Neural Networks by : John Larry Stricker
Download or read book Executive Functions and Constructive Neural Networks written by John Larry Stricker and published by . This book was released on 2004 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present work explores how executive functions can be implemented in neural networks. Computational models such as neural networks allow researchers to develop more sophisticated conceptualizations of how executive functioning could be implemented in the brain. However, most computational models are designed only to solve a single problem rather than to solve multiple problems and integrate new and old knowledge. The problem domain for the models of the present work consists of Boolean logic expressions. These expressions easily lend themselves to implementation in neural networks while at the same time they can represent a range of problems that relate to executive functions, such as learning complementary vs. unrelated information. Network architectures and training regimes are developed that allow neural networks to solve multiple problems constructively while minimizing the impact of interference. The networks illustrate that the constructive learning of multiple problems does not require an executive controller, separate memory systems, or the constructive addition of learning resources.
Book Synopsis Constructive Learning by : Rajesh Girish Parekh
Download or read book Constructive Learning written by Rajesh Girish Parekh and published by . This book was released on 1998 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on two important areas of machine learning research - regular grammar inference and constructive neural network learning algorithms. Regular grammar inference is the process of learning a target regular grammar or equivalently a deterministic finite state automaton (DFA) from labeled examples. We focus on the design of efficient algorithms for learning DFA where the learner is provided with a representative set of examples for the target concept and additionally might be guided by a teacher who answers membership queries. DFA learning algorithms typically map a given structurally complete set of examples to a lattice of finite state automata. Explicit enumeration of this lattice is practically infeasible.
Book Synopsis Structure Learning with Constructive Neural Networks by : Jani Lahnajärvi
Download or read book Structure Learning with Constructive Neural Networks written by Jani Lahnajärvi and published by . This book was released on 2001 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural Network Learning and Expert Systems by : Stephen I. Gallant
Download or read book Neural Network Learning and Expert Systems written by Stephen I. Gallant and published by MIT Press. This book was released on 1993 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: presents a unified and in-depth development of neural network learning algorithms and neural network expert systems
Book Synopsis Continual Robot Learning with Constructive Neural Networks by : A.Poli Grossmann (R)
Download or read book Continual Robot Learning with Constructive Neural Networks written by A.Poli Grossmann (R) and published by . This book was released on 1997 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Constructive Algorithms for Structure Learning in Feedforward Neural Networks by : Tin-yau Kwok
Download or read book Constructive Algorithms for Structure Learning in Feedforward Neural Networks written by Tin-yau Kwok and published by . This book was released on 1996 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Constructive Neural Network Training Algorithms for Pattern Classification Using Computational Geometry Techniques by : Steven A. Young
Download or read book Constructive Neural Network Training Algorithms for Pattern Classification Using Computational Geometry Techniques written by Steven A. Young and published by . This book was released on 1996 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Constructive Neural Network Incorporating Competitive Learning of Locally Tuned Hidden Neurons by : Simon William D'Alton
Download or read book A Constructive Neural Network Incorporating Competitive Learning of Locally Tuned Hidden Neurons written by Simon William D'Alton and published by . This book was released on 2005 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning Reactive Behaviors with Constructive Neural Networks in Mobile Robotics by : Jun Li
Download or read book Learning Reactive Behaviors with Constructive Neural Networks in Mobile Robotics written by Jun Li and published by . This book was released on 2006 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Constructive Neural Networks for Function Approximation and Their Application to CFD Shape Optimization by : Adeline Schmitz
Download or read book Constructive Neural Networks for Function Approximation and Their Application to CFD Shape Optimization written by Adeline Schmitz and published by . This book was released on 2007 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Areas of further research are discussed and include investigating other types of network committees as well as modifying the optimizer itself.
Book Synopsis Learning in Fractured Problems with Constructive Neural Network Algorithms by : Nate F. Kohl
Download or read book Learning in Fractured Problems with Constructive Neural Network Algorithms written by Nate F. Kohl and published by . This book was released on 2009 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems -- such as those involving strategic decision-making -- have remained difficult to solve. This dissertation proposes the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. To evaluate this hypothesis, a method for measuring fracture using the concept of function variation of optimal policies is proposed. This metric is used to evaluate a popular neuroevolution algorithm, NEAT, empirically on a set of fractured problems. The results show that (1) NEAT does not usually perform well on such problems, and (2) the reason is that NEAT does not usually generate local decision regions, which would be useful in constructing a fractured decision boundary. To address this issue, two neuroevolution algorithms that model local decision regions are proposed: RBF-NEAT, which biases structural search by adding basis-function nodes, and Cascade-NEAT, which constrains structural search by constructing cascaded topologies. These algorithms are compared to NEAT on a set of fractured problems, demonstrating that this approach can improve performance significantly. A meta-level algorithm, SNAP-NEAT, is then developed to combine the strengths of NEAT, RBF-NEAT, and Cascade-NEAT. An evaluation in a set of benchmark problems shows that it is possible to achieve good performance even when it is not known a priori whether a problem is fractured or not. A final empirical comparison of these methods demonstrates that they can scale up to real-world tasks like keepaway and half-field soccer. These results shed new light on why constructive neuroevolution algorithms have difficulty in certain domains and illustrate how bias and constraint can be used to improve performance. Thus, this dissertation shows how neuroevolution can be scaled up from learning low-level control to learning strategic decision-making problems.
Book Synopsis Constructive Training Methods for Feedforward Neural Networks with Binary Weights by : E. Mayoraz
Download or read book Constructive Training Methods for Feedforward Neural Networks with Binary Weights written by E. Mayoraz and published by . This book was released on 1995 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Constructive Neural Networks with Applications to Image Compression and Pattern Recognition by : Liying Ma
Download or read book Constructive Neural Networks with Applications to Image Compression and Pattern Recognition written by Liying Ma and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of Neural Networks (NNs) has witnessed a striking progress in the past fifteen years. The basic issues, such as determining the structure and size of the network, and developing efficient training/learning strategies have been extensively investigated. This thesis is mainly focused on constructive neural networks and their applications to regression, image compression and pattern recognition problems. The contributions of this work are as follows. First, two new strategies are proposed for a constructive One-Hidden-Layer Feedforward NN (OHL-FNN) that grows from a small initial network with a few hidden units to one that has sufficient number of hidden units as required by the underlying mapping problem. The first strategy denoted as error scaling is designed to improve the training efficiency and generalization performance of the OHL-FNN. The second strategy is a pruning criterion that produces a smaller network while not degrading the generalization capability of the network. Second, a novel strategy at the structure level adaptation is proposed for constructing, multi-hidden-layer FNNs. By utilizing the proposed scheme, a FNN is obtained that has sufficient number of hidden layers and hidden units that are required by the complexity of the mapping being considered. Third, a new constructive OHL-FNN at the functional level adaptation is developed. According to this scheme, each hidden unit uses a polynomial as its activation function that is different from those of the other units. This permits the growing network to employ different activation functions so that the network would be able to represent and capture the underlying map more efficiently as compared to the fixed activation function networks. Finally the proposed error scaling and input-side pruning techniques are applied to regression, still and moving image compression, and facial expression recognition problems. The proposed constructive algorithm for creating multilayer FNNs is applied to a range of regression problems. The proposed polynomial OHL-FNN is utilized to solve both regression and classification problems. It has been shown through extensive simulations that all the proposed techniques and networks produce very promising results.
