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Topic: Node (neural networks)

	Neural Networks
		Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.
		The commonest type of artificial neural network consists of three groups, or layers, of units: a layer of "input" units is connected to a layer of "hidden" units, which is connected to a layer of "output" units.
		Neural networks were used to discover the influence of undefined interactions by the various variables.
	www.doc.ic.ac.uk /~nd/surprise_96/journal/vol4/cs11/report.html (6614 words)

	Neural network - Encyclopedia.WorldSearch (Site not responding. Last check: 2007-10-22)
		In a neural network model, simple nodes (or "neurons", or "units") are connected together to form a network of nodes — hence the term "neural network".
		Neural networks are particularly useful for dealing with bounded real-valued data, where a real-valued output is desired; in this way neural networks will perform classification by degrees, and are capable of expressing values equivalent to "not sure".
		The genome is then constituted of the networks parameters and the fitness of a network is the adequacy of the behaviour exhibited by the controlled robot (or often by a simulation of this behaviour).
	encyclopedia.worldsearch.com /neural_network.htm (2823 words)

	Neural Networks & Connectionist Systems
		Neural models of intelligence emphasize the brain's ability to adapt to the world in which it is situated by modifying the relationships between individual neurons.
		Neural networks are usually characterized in terms of the number and types of connections between individual processing elements, called neurons, and the learning rules used when data is presented to the network.
		Neural networks, unlike fuzzy logic, seek to reproduce the versatility of the human brain in recognizing the end-to-end, input-to-output behavior of a system without understanding all the processes taking place within it.
	www.aaai.org /AITopics/html/neural.html (4429 words)

	Neural networks - Science Articles :: Physics Post
		Each node in the next layer then receives a value which is the sum of the values produced by the connections leading into it, and in each node a simple computation is performed on the value - a sigmoid function is typical.
		If the neural network is trained using the cross-entropy error function (see Bishop's book) and if the neural network output has a sigmoidal non-linear, then the outputs will be estimates of the true posterior probability of a class.
		The genome is then constitued of the networks parameters and the fitness of a network is the adequacy of the behaviour exhibited by the controlled robot (or often by a simulation of this behaviour).
	www.physicspost.com /science-article-191.html (836 words)

	AIEVOLUTION (Site not responding. Last check: 2007-10-22)
		The neural networks of the human brain are almost to complex to comprehend: let alone re-create it in silicon microprocessors.
		Neural networks are a simulation of the processes of the human brain.
		Neural networks can be built to solve specific problems, but they are most useful for their ability to learn.
	www.teklearning.com /ai_evo.asp (2692 words)

	Mind and Machine : Neural networks
		Each unit or node is a simplified model of a real neuron which fires (sends off a new signal) if it receives a sufficiently strong input signal from the other nodes to which it is connected.
		The strength of these connections may be varied in order for the network to perform different tasks corresponding to different patterns of node firing activity.
		Neural networks are very different - they are composed of many rather feeble processing units which are connected into a network.
	www.phy.syr.edu /courses/modules/MM/n_net/n_net.html (503 words)

	An introduction to neural networks
		Limiting ourselves to nets with no hidden nodes, but possibly having more than one output node, let p be an element in a training set, and t(p,n) be the corresponding target of output node n.
		When output nodes take their inputs from hidden nodes, and the net finds that it is in error, its weight adjustments require an algorithm that will pick out how much the various nodes contributed to its overall error.
		See Neural Networks at your Fingertips for a set of C packages that illustrate Adaline networks, back-propagation, the Hopfield model, and others.
	www.ibm.com /developerworks/library/l-neural (3400 words)

	Prostate Calculator - Recent Journal Articles and Proceedings
		An artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer.
		Artificial neural network model to predict risk of non-organ-confined disease and risk of lymph node spread in men with clinically localized prostate cancer [abstract].
		Use of artificial neural networks in the clinical staging of prostate cancer: Implications for prostate brachytherapy.
	www.prostatecalculator.org /about-pubs.html (853 words)

	Graphical Models
		Hidden nodes are nodes whose values are not known, and are depicted as unshaded; observed nodes (the ones we condition on) are shaded.
		Finally, consider the case in which we have one incoming and outgoing arrow to X. It is intuitive that the nodes upstream and downstream of X are dependent iff X is hidden, because conditioning on a node breaks the graph at that point.
		Of course, interpreting the ``meaning'' of hidden nodes is always tricky, especially since they are often unidentifiable, e.g., we can often switch the interpretation of the true and false states (assuming for simplicity that the hidden node is binary) provided we also permute the parameters appropriately.
	www.cs.ubc.ca /~murphyk/Bayes/bayes.html (6598 words)

	Neural Networks Software
		Essays on issues relating to the nature of consciousness, including neuro-physiology, quantum physics and neural networks.
		An interdisciplinary journal dealing with artificial intelligence and its applications, artificial neural nets and their applications, cognitive science, brain models and their applications, and the social context of intelligent systems.
		A bi-monthly, genetic algorithms, neural networks, including chaos, cellular automata, cross-disciplinary journal focusing on the science of complex adaptive systems and evolutionary game theory.
	www.mygooglepagerank.com /IA/Neural_Networks_Software.htm (370 words)

	FAQ's about neural networks to predict prostate cancer
		For example, our lymph node spread model was developed using data from thousands of patients who had surgery to remove their prostates at the Johns Hopkins Medical Institutions.
		Finally, a number emerges at the output node with a value that depends on the input values and the weights assigned to each interconnection.
		Further, this model is designed to predict the risk of lymph node spread in men with clinically localized prostate cancer only (not for advanced disease or for men who have not been diagnosed with prostate cancer).
	www.prostatecalculator.org /howItWorks.html (835 words)

	Neural Network Course
		Logical Designs gives courses on site for neural network applications.
		Fusion and Management Dual Node Network (DNN) Architecture
		Fighter-Based DFandRM (radar, IR, IFF, RWR, MWS, CNI) Networks
	www.logicaldesigns.com /Course1.htm (178 words)

	internet culture
		It is a system that is alive, whether or not it possesses all the attributes needed for an organism.''[11, page 84] Gaia is not only alive but it is coming to have a mind, thanks to the Internet and other networking technologies.
		There is a sense in which a global mind also emerges in a network culture.
		Our primary difficulty in comprehending the global mind of a network culture will be that it does not have a central ``I'' to appeal to.
	www.brandeis.edu /pubs/jove/HTML/V6/iculture.html (8643 words)

	Bibliography for Artificial Intelligence: A Modern Approach
		Blum, A. and Rivest, R. Training a 3-node neural network is NP-complete.
		Cybenko, G. Continuous valued neural networks with two hidden layers are sufficient.
		Johnston, M. and Adorf, H.-M. Scheduling with neural networks: the case of the Hubble space telescope.
	www.cs.berkeley.edu /~russell/aima-bib.html (8513 words)

	Current Research Focusing on Euchromatin Within the Cell Nucleus.
		Cantile M, Cindolo L, Napodano G, Altieri V, and Cillo C, "Hyperexpression of locus C genes in the HOX network is strongly associated in vivo with human bladder transitional cell carcinomas".
		Goldberg-Cohen I, Furneauxb H, and Levy AP, "A 40-bp RNA Element That Mediates Stabilization of Vascular Endothelial Growth Factor mRNA by HuR".
		Von Dassow G, Meir E, Munro EM, and Odell GM, "The Segment Polarity Network is a Robust Developmental Module".
	www.euchromatin.net /current1.html (9675 words)

	ITTC Publications
		Neural Network-Based Classification of Scenes from SAR Images Using Spectral Information: An Empirical Study,
		Luiz A. DaSilva, David W. Petr, Nail Akar; Proceedings of Conference on Performance and Control of Network Systems II, part of the SPIE (International Society of Optical Engineering) International Symposium on Voice, Video and Data Communication; Nov. 1998, pp.
		Victor S. Frost; Network Management into the 21st Century, S. Aidarous, T. Plevyak (eds.); IEEE Press, 1994.
	www.ittc.ku.edu /publications/index.phtml (10285 words)

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