The generalization error of a perceptron is the probability of the student perceptron to classify an example differently from the teacher and is given by the overlap of the student and teacher synaptic vectors and is a function of their scalar ...
Generalization of Class Composition Hierarchies
An attribute of an object may be composed of or described by another object, some of whose attributes may be in turn composed of or described by other objects, ...
Generalization
Why is overtraining a problem? The whole purpose of training these nets is to be able to predict the function output (regression) or class (classification) for inputs that the net has never seen before (i.e. was not trained on).
Generalization of the concept of "Confidence level"
The above definition of a confidence level implicitely assumes that the probability for the interval to cover θ0 does not depend on the value of θ0.
Generalization
Generalization is the ability of a neural network to produce reasonable responses to input patterns that are similar, but not identical, to training patterns.
generalization in backprop Learning in backprop seems to operate by first of all getting a rough set of weights which fit the training patterns in a general sort of way, ...
Generalizations
For any model of computations there exist simple analogs for Busy Beaver.
Allen Brady has determined Busy Beaver for machines with 3 states and 3-letter alphabet S(3,3)≥92649163.
generalization given a particular instance or data set, to represent a more general case that includes the instance.
"A Generalization of Kakutani's Fixed-Point Theorem," Bachelor's Thesis in Mathematics, Harvard, 1950. This thesis was about the topology of fixed points of continuous functions on spheres, using new arguments about knots in 3-spheres.
Bad Generalization
In a pattern classification problem there is no guarantee that your backprop network is going to come up with a sensible way to partition the boundaries between classes.
Stacked Generalization
See Stacking.
Stacking (Stacked Generalization)
The concept of stacking (Stacked Generalization) applies to the area of predictive data mining, to combine the predictions from multiple models.
Table 5: Generalization of Cross-overs Between Parents
There are many other ways in which parents can trade genetic information to create an offspring, crossing over is just one way.
Lebowitz, M. "Generalization and Memory in an Integrated Understanding System." Ph.D. Thesis, Yale University, Department of Computer Science, 1980.
Newell, Allen. Unified Theories of Cognition. Cambridge, MA: Harvard University Press, 1990.
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Generalization and Symbolic Processing in Neural Networks
Word Learning in Feature Space
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Labels As an Accelerator of Ontological Development ...
Some tentative generalizations about what philosophy is
So philosophy, it seems, is a discipline that draws on knowledge that the average educated person has, and it does not make use of experimentation and careful observation, ...
Minkowski mean is a generalization of arithmetic, quadratic and harmonic mean.
Minkowski mean is defined as
Example: ...
" Prediction machinery is ultimately theory-making machinery -- devices for generating abstractions and generalizations. Prediction machinery chews on the mess of seemingly random chicken-scratch data produced by complex and living things.
Generalization: A measure of how well a network can respond to new images on which it has not been trained but which are related in some way to the training patterns.
Programs [a mere sampling of what's available on the Web] "The ADIOS (Automatic DIstillation of Structure) algorithm relies on a statistical method for pattern extraction (The MEX algorithm) and on structured generalization, ...
Factor analysis A generalization of PCA which is based explicitly on maximum-likelihood. Like PCA, each data point is assumed to arise from sampling a point in a subspace and then perturbing it with full-dimensional Gaussian noise.
problems, or making generalizations and relationships? And what about perception and comprehension? Research into the areas of learning, of language, and of sensory perception have aided scientists in building intelligent machines.
Generalization is done by replacing all the constants in the macro-operators with variables.The STRIPS, for example, is a planning algorithm that employed macro-operators in it's learning phase.
The Tietjen-Moore test is a generalization of the Grubbs' test to the case of multiple outliers. If testing for a single outlier, the Tietjen-Moore test is equivalent to the Grubbs' test.
Where, again, reasonable is defined by the user but is difficult to quantify beyond that except to say that just one cluster is unacceptable (too much generalization) and that as many clusters and original records is also unacceptable Many ...
4 Generalization in Reinforcement Learning . . . . . . . . . . . . . . . . . 845
21.5 Policy Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848
21.6 Applications of Reinforcement Learning . . . . . . . . . . . . . . . . . .
Size: the representation of the approximate function can be significantly smaller than the true function.
Generalization: the approximate function can be used on inputs for which we do not know the value of the function.
Usually, the network is trained for a fixed number of steps and the performance is measured on an "evaluation set" data. By using different sets of data for training and evaluation, networks with better generalization abilities are given a ...
The theory of fuzzy logic gives an inference method under cognitive uncertainty, computational neural networks, fuzzy logic offer exciting benefits such as learning, adaptation, fault tolerance, parallelism, and generalization.
 See also: Distribution, Neural network, Regression, Classification, Estimation
  
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