Blood-Clot
发表于 2025-3-25 06:24:33
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继承人
发表于 2025-3-25 10:16:41
Product Units with Trainable Exponents and Multi-Layer Networkstead of the output of this unit depending on a weighted sum of the inputs, it depends on a weighted product. In justifying the introduction of a new type of unit we explore at some length the rationale behind the use of multi-layer neural networks, and the properties of the computational units withi
wangle
发表于 2025-3-25 11:53:46
Optimization of the number of hidden cells in a multilayer perceptron. Validation in the linear casea given task. An incremental algorithm has been proposed which is proven to converge. It essentially works for binary outputs and does not necessarily provide the minimal size solution. We propose another approach, in the general case of continuous neurons.
conformity
发表于 2025-3-25 17:08:42
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Concomitant
发表于 2025-3-25 21:37:45
Synchronous Boltzmann Machines and Gibbs Fields: Learning Algorithmsem as pattern classifiers that learn, have proposed a learning algorithm for the asynchronous machine. Here we study the synchronous machine where all neurons are simultaneously updated, we compute its equilibrium energy, and propose a synchronous learning algorithm based on . average coactivity of
爱社交
发表于 2025-3-26 03:38:08
Learning algorithms in neural networks: recent resultsor perception. The algorithm optimizes the stability of learned patterns, which enlarges the size of the basins of attraction. The second algorithm builds a multilayer feedforward network: it allows one to learn an arbitrary mapping input → output. The convergence of the growth process is guaranteed
拉开这车床
发表于 2025-3-26 04:47:36
Statistical approach to the Jutten-Hérault algorithmith guidance provided by neurosciences analogies, the unsupervised learning JH algorithm has been adjusted and implemented on an array of p linear neurons totally interconnected . Because of its numerous applications ranging from image processing to antenna array processing, the JH algorithm
IST
发表于 2025-3-26 10:38:43
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Cuisine
发表于 2025-3-26 13:00:08
Neural Networks Dynamicss (binary states) and also for continuous local rules. By doing so we prove, in the context of binary networks, the convergence to cycles of period one or two for symmetric weights. Also we prove that “almost symmetric” Neural Networks admit large cycles (non-bounded in the size, n, of the array) an
lethargy
发表于 2025-3-26 18:49:21
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