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	[No title]
		A number of parameters for the boltzmann machine is under user control, such as: number of nodes, learning rate, choice of training algorithm, choice of training patterns, termination criterion etc. The details are given with the description of the file formats.
		The function readinputs() reads the machine parameters (#layers, #nodes in each layer, and internal connections of each layer), calls functions from the common module to generate the machine structure, and then reads in the weights of the machine (which were presumably calculated by the boltztrain program).
		The anneal_style() function initializes the state of the machine to a random state, copies the inputs to the input nodes, clamps the input layer, sets the start temperature, the end temperature and the beta parameter to 20.0, 0.1 and 0.95 respectively, and calls the annealing function from the common module (anneal()).
	www.cis.syr.edu /~mohan/html/Bookfiles/README-for-boltz (3687 words)

	[No title]
		The neural network may be a Boltzmann Machine type of neural network comprising neurons arranged in an input layer, a hidden layer, and an output layer.
		The system of claim 1 wherein said neural network is a Boltzmann Machine type neural network and said neural network comprises a hidden layer of neurons connected between said input layer of neurons and said output layer of neurons.
		The Boltzmann Machine type neural network typically comprises an input layer of neurons, an output layer of neurons, and a hidden layer of neurons in between the input layer and output layer.
	ece-www.colorado.edu /~timxb/timxb/publications/96pat.txt (5601 words)

	the analog/digital distinction - IV - thermodynamic functionalism
		A Boltzmann machine is a system of stochastic binary units in which the computational processes are iterative and local (that is, they concern the repeated mutual adjustments of neighbouring units).
		As long as the Boltzmann machine is being simulated on a serial digital architecture, though, thermodynamic description is merely a mathematical structure implemented in a code which is implemented in programs running on a digital computer.
		Connection machines are a step closer to intrinsic content in just one way--their physical causal systematicity is in some small degree provided by their self-organization in response to input samples.
	www.sfu.ca /~elfreda/theory/analog/analog7.html (9037 words)

	Bobby Rohrkemper: Computational Physics Introduction
		This program, entitled the Boltzmann Machine, simulates molecules of an ideal gas moving at a constant temperature through 2 different potentials.
		Growing from a Molecular Display template written by Dr. Tobochnik, the Boltzmann Machine was written to simulate an experiment from the December 2000 issue of the American Journal of Physics.
		In addition, when the potential difference is constant and the ratio of the areas of the two levels is changed, a linear relationship exists between the ratio of the two areas and the Boltzmann Ratio.
	people.brandeis.edu /~bobby/pfolio/computationalPhysicsIntroduction.htm (1610 words)

	ANNOTATED BIBLIOGRAPHY OF MIND-RELATED TOPICS
		Boltzmann's model of a gas represents a discrete set of molecules as a continuum of points; 2.
		The generalized "delta rule" was basically an adaptation of the Widrow-Hoff error correction rule to the case of multi-layered networks by moving backwards from the output layer to the input layer.
		Both machines and living beings tend to change to compensate variations in the environment, so that the combined system is stable.
	www.thymos.com /mind/a.html (2689 words)

	British Computer Society (BCS) (Site not responding. Last check: 2007-10-13)
		At their most basic level, graphical models contain a set of nodes, a set of edges between the nodes, and a function that is both associated with the graph and constrained by the edges.
		In addition, many of the algorithms used for Boltzmann machines are specialized versions of algorithms that apply to other graphical models, and so much of the material in these chapters serves as an introduction for later papers.
		The structure of the book also provides a picture of the history of work on graphical models, as only one of the Boltzmann machine papers was written after 1994, while only one of the non-Boltzmann papers was written before 1994.
	www.dcs.ex.ac.uk /bcs-par/newsletter.htm (1286 words)

	[No title]
		The Boltzmann machine is a stochastic version of Hopfield's energy minimizing net which is capable of almost guaranteed convergence to the global minimum of an arbitrary bounded quadratic energy function.
		Simulated annealing is also applied to optimal weight learning in generally interconnected multilayer Boltzmann machines, thus extending the applicability of the Boltzmann machine from combinatorial optimization to optimal supervised learning of complex binary mappings.
		Mean-field annealing is a deterministic approximation (based on mean-field theory) to stochastic simulated annealing where the mean behavior of the stochastic state transitions are used to characterize the Boltzmann machine.
	neuron.eng.wayne.edu /tarek/MITbook/chap8/8_7.html (1441 words)

	PSYCH3W03/NEURCOMP3W03 Hopfield networks and Boltzmann machines
		The stochasticity enables the Boltzmann machine to overcome the problem of getting stuck in local energy minima, while the contrastive Hebb rule allows the network to be trained with hidden features and thus overcomes the capacity limitations of the Hopfield network.
		However, in practice, learning in the Boltzmann machine is hopelessly slow.
		In class, we saw a demonstration on overhead transparencies of the Boltzmann machine performing figure-ground segregation.
	www.psychology.mcmaster.ca /3W03/hopfield.html (1166 words)

	Untitled (Site not responding. Last check: 2007-10-13)
		The machine is said to have a temperature, and this related to the probability.
		Boltzmann Machines attempt to solve the same problem as back propagation.
		Boltzmann Machines are more computationally expensive than back propagation but they also give better results.
	www.comp.leeds.ac.uk /ugadmit/cogsci/tech/hopn.htm (561 words)

	Abstracts of Cardiac Diagnosis Papers (Site not responding. Last check: 2007-10-13)
		A brief account of the theory (based upon the so-called Boltzmann machine) underlying this little-known network is presented.
		The Boltzmann perceptron network is trained to diagnose the presence or absence of myocardial infarction on data gathered from a large UK teaching hospital and is found to perform as well as senior registrars with specific cardiological training (diagnosis accuracy in excess of 80%).
		In addition, the Boltzmann perceptron network is found to provide greater user confidence than the multilayer perceptron.
	www-ksl.stanford.edu /projects/mis204/heart-dx-abstracts.html (529 words)

	[No title]
		The Boltzmann machine is a mathematical model making it possible to capture this notion on a computer.
		If trained on natural grey-valued images and using ``sparse coding'' then artificial neurons develop properties which are known from neurons of the primary visual cortex: their connections to the input are limited to a small area and their shape is designed to capture edges of a certain orientation.
		From the example of a Boltzmann machine we see that artificial neural nets are not only models of information processing in the brain, but also come up with attractive models for biological learning mechanisms.
	www.his.sunderland.ac.uk /~cs0cwe/mywork_BM.html (776 words)

	[No title]
		Generally speaking, such approximations are adequate in high dimensional systems of many interacting units (states) where each state is a function of all or a large number of other states allowing the central limit theorem to be used (see Problem 7.5.9).
		In this section, we restrict our discussion of mean field annealing to the Boltzmann machine which was introduced in the previous section.
		The deterministic Boltzmann machine is applicable only to problems involving quadratic cost functions.
	neuron.eng.wayne.edu /tarek/MITbook/chap8/8_4.html (792 words)

	sol9_2003
		The Boltzmann machine and sigmoid belief network share a common feature: they are both stochastic machines with their theory rooted in statistical mechanics.
		The Boltzmann machine is a recurrent network whereas the sigmoid belief network is an acyclic feedforward network.
		The learning process in a Boltzmann machine involves two phases: one clamped (positive) and the other free running (negative).
	www.cis.hut.fi /Opinnot/T-61.263/sol9_2003 (200 words)

	Conference Materials (Site not responding. Last check: 2007-10-13)
		Boltzmann Machine Learning Using Mean Field Theory and Linear Response Correction
		The learning process in Boltzmann Machines is computationally intractible.
		We present a new approximate learning algorithm for Boltzmann Machines, which is based on mean field theory and the linear response theorem.
	cognet.mit.edu /library/conferences/paper?paper_id=1989 (102 words)

	Boltzmann machines (Site not responding. Last check: 2007-10-13)
		Incidentally, the name 'Boltzmann' was chosen in honour of the great Austrian phyisicist Ludwig Boltzmann, who showed that the random motion of the molecules of a gas had an energy directly proportional to the temperature of the gas.
		Cf the ball in the energy landscape analogy - agitating the energy landscape is more likely to help the ball find a true energy well - a genuine low point in the energy landscape.
		Three differences between Boltzmann machine and the previous regime for constraint satisfaction nets, the schema model.
	www.mdx.ac.uk /www/psychology/cog/psy3250/Boltz/boltz.htm (1054 words)

	Neural Network Systems Techniques and Applications: Implementation Techniques
		A Boltzmann machine without any hidden layers can be viewed as a stochastic version of a Hopfield network.
		This type of network is particularly interesting from a statistical point of view in that it can be used to estimate the parameters of an exponential probability distribution.
		This chapter is more practical than the first chapter in that it provides some simple numerical examples of the training methods and draws conclusions based on the results.
	www.innovatia.com /software/papers/reviews/imptechs.htm (908 words)

	PC AI 16.4 Paid Version Page 64
		Because most machines are designed to perform a function reliably, it is unlikely that a machine that has emotions is desirable where reliability is a concern.
		There are possible functions that an emotional or creative machine could perform and where reliability and repeatability are not issues.
		To do this, a machine must have the ability to discover and manipulate relationships, a key to intel-ligent thought.
	www.pcai.com /Paid/Issues/PCAI-Online-Issues/16.4_OL/New_Folder/NI0618/16.4_PA/PCAI-16.4-Paid-pg.64-Art8.htm (621 words)

	Attrasoft Boltzmann machine (ABM)/chap. 1 (Site not responding. Last check: 2007-10-13)
		The Boltzmann Machine is closely related to the Hopfield model.
		The Boltzmann Machine is a special type of neural network, in which each neuron configuration has a certain probability to appear.
		(The name comes from the following fact: the Boltzmann Machine is a probabilistic neural network which forms a Markov chain; the invariant distribution of the Markov chain is similar to the "Boltzmann Distribution" in statistical physics).
	attrasoft.com /abm/chap27_1.html (1554 words)

	[No title]
		Should be 3 / int states; / State of the machine: states[layer][node] / int clamped; /* Is the i-th layer clamped, i.e.
		Net input is calculated as a function of the mean states of the surrounding nodes / double get_mean_input(p, layer, node) struct machine* p; int layer, node; { double sum = 0; int j; / Connections from prev.
		Collect statistics for the input / void phase1(p,n) struct machine* p; int n; { int i,j,k; int inlayer = 0; int outlayer = p->layers-1; int num_updates = 10; int total_updates; / Initialize the prob.
	www.cis.syr.edu /~mohan/html/Bookfiles/boltztrain.c (2640 words)

	Libann: Example Programs
		One application of a Boltzmann machine is its use as a classifier.
		The program creates Boltzmann machine which recognises what each of these characters look like, and then presents it with another similar glyph from each class.
		Looking up values in the Boltzmann machine is simply a matter of presenting the value to the
	www.nongnu.org /libann/doc/libann_6.html (1549 words)

	Dr. Dobb's \| C Programming \| July 22, 2001
		Since it learns, a Boltzmann Machine is a (parallel) implementation of some sort of minimization algorithm.
		This is just a random search, so a Boltzmann Machine, if you look at it as a minimization algorithm, is a parallel implementation of a random search.
		If you have access to the weights, and all you're doing is learning physics or doing circuit board layout, it is much quicker to just kick all the weights randomly, stay in the task subspace, do a Boltzmann Machine followed by steepest descent, and repeat.
	www.ddj.com /184408224 (4380 words)

	Symmetry breaking and training from incomplete data with radial basis Boltzmann machines (Site not responding. Last check: 2007-10-13)
		Nijman, M.J., and Kappen, H.J. Symmetry breaking and training from incomplete data with radial basis Boltzmann machines.
		A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist.
		We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.
	www.nici.kun.nl /Publications/1997/11172.html (228 words)

	[No title] (Site not responding. Last check: 2007-10-13)
		Appendix K A Simple Boltzmann Machine This program implements a simple Boltzmann machine.
		The program itself is a straightforward realization of the dynamics of a Boltzmann network as discussed in Chapter 12.
		Any complexity in the program is primarily due to the human factors and the display, as is usually the case.
	www.cog.brown.edu:16080 /courses/cg102/appendices/appk.txt (647 words)

	A Toy Problem
		Figure: A Boltzmann machine ring of 200 nodes is initialized with thresholds and weights as shown in the lower part of the figure.
		Exact means are plotted as well as the results of the bound propagation algorithm.
		Nodes are connected as in the Boltzmann machine with a weight
	www.cs.cmu.edu /afs/cs.cmu.edu/project/jair/pub/volume19/leisink03a-html/node8.html (169 words)

	LENS Manual: Group Input Types
		In other words, it takes the sum over all incoming links of the product of the link weight and the output of the unit the link is coming from.
		This is the default unless the network is a BOLTZMANN machine.
		This is the input half of a Boltzmann unit.
	tedlab.mit.edu /~dr/Lens/inputTypes.html (664 words)

	EETimes.com - New system teaches itself to see
		A type of network called a Restricted Boltzmann Machine (RBM) is built upon the idea of learning and recognizing through a "Product of Experts" — a concept that is thought to be more biologically valid than its predecessors.
		Though it is easy to generate pictures from, say, a 3-D model, it is not easy to reconstruct the 3-D model from this data.
		In fact, this is why machine vision is so difficult in the first place.
	www.eetimes.com /story/OEG20001218S0055 (1903 words)

	Student wins engineering research prize (Site not responding. Last check: 2007-10-13)
		The Boltzmann machine is a type of neural network that can find near optimum solutions to certain types of problem, which are expressed in the form of a graph.
		Ahmet successfully developed a model of the extremely complicated behaviour of the hardware Boltzmann Machine, and to compare it against the theoretical best solution produced by the even more complex Edmonds Algorithm.
		Professor Edward Stansfield from Thales Research, who is a visiting professor at the University of Reading, said he was very impressed with the overall standard of the projects.
	www.fp.rdg.ac.uk /news/details.asp?ID=396 (233 words)

	The Boltzmann Machine: Necker Cube Example (Site not responding. Last check: 2007-10-13)
		The Necker cube provides a useful paradign for demonstrating the properties of constraint satisfaction networks (Feldman, 1981) such as the Boltzmann machine (McClelland & Rumelhart, 1988).
		In the following simulation two sets of eight units represent the alternate interpretations of the cube.
		Figure 1: The Necker cube example of the Boltzmann machine.
	www.cs.cf.ac.uk /Dave/JAVA/boltzman/Necker.html (545 words)

	CS 152 Reference Links, Fall 2000
		Machine Learning, Neural and Statistical Classification (D. Michie, D.J. Spiegelhalter, C.C. Taylor, eds.)
		Turing Machines are Recurrent Neural Networks (Heikki Hyötyniemi)
		The use of recurrent neural networks in continuous speech recognition
	www.cs.hmc.edu /courses/mostRecent/cs152/links.html (219 words)

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