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Topic: Overfits

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Neural networks

	Overfitting - Wikipedia, the free encyclopedia
		In statistics, overfitting is fitting a statistical model that has too many parameters.
		Overfitting is generally recognized to be a violation of Occam's razor.
		In this process of overfitting, the performance on the training examples still increases while the performance on unseen data becomes worse.
	en.wikipedia.org /wiki/Overfitting (271 words)

	Overfitting -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-10-21)
		In (A branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters) statistics, overfitting is fitting a statistical model that has too many parameters.
		Overfitting is generally recognized to be a violation of (Click link for more info and facts about Occam's razor) Occam's razor.
		The concept of overfitting is important also in (Click link for more info and facts about machine learning) machine learning.
	www.absoluteastronomy.com /encyclopedia/o/ov/overfitting.htm (315 words)

	Backprop Learning Tool
		In this paper, we demonstrate that it is sometimes possible to use all available data for training a large network (a network capable of overfitting the data) and yet still determine an appropriate stopping point to ensure that the network generalizes properly.
		The reason for this phenomenon is that the network "overfits" the data, i.e., the network tries to fit the noise in the data as well as the underlying function to be approximated.
		In this case, it is possible to overestimate the number of required hidden units, relying on the validation process to prevent the network from overfitting the data.
	neuron.eng.wayne.edu /bpFunctionApprox/bpFunctionApprox.html (1695 words)

	Apparatus for orienting and printing capsules - Patent 4632028
		The apparatus of claim 29 wherein said articles are capsules having a body portion and a cap portion telescopically overfitting said body portion, and wherein the bases of said pockets are sized to slidingly receive the body portions of said capsules and to frictionally engage the cap portions of said capsules.
		A back guide 23 overfits the rectifying drum 10 in the vicinity of this transition to guide the capsules 2 into their desired position, and to make sure that the capsules 2 are retained within the pockets 11 as they traverse the lower portions of the rectifying drum 10.
		The fingers 90 are caused to reciprocate as the apparatus 1 is operated, since such reciprocation has been found to be advantageous in assuring that the capsules 2 are efficiently delivered to the rectifying drum 10, further reducing the potential for a pocket 11 to pass beneath the hopper 5 without receiving a capsule 2.
	www.freepatentsonline.com /4632028.html (6963 words)

	Rectilinear convex region
		The training data is considered the recorded data from which we derive rules, and the test data is considered the unobserved data whose features are predicted by the derived rules.
		As a result, it is presumed that the difference between a training confidence and a test confidence is much larger in X-monotone regions than that in the other two classes of region and that the test confidence of X-monotone regions may not be the highest.
		It is presumed that the difference between training and test confidences of rectilinear regions is smaller compared to that of X-monotone regions and that the test confidence of rectilinear regions is higher than that of rectangular regions.
	www.research.ibm.com /trl/projects/s7800/DBmining/num2d/rc.htm (2283 words)

	Mushroom hook cap for borescope - Patent 5052803
		In one preferred embodiment, the insertion tube has a steerable bending section disposed adjacent to the tip and which is bendable in one bending plane under control of a control unit coupled to the proximal end of the insertion tube.
		The cap 16 has a generally tubular sleeve 18 that overfits the borescope tip 14 and a radial flange 20 that extends outward from the distal end of the sleeve 18.
		At the back or proximal end of the insertion tube 12 is a steering and control unit 22 that can be manipulated by an operator to bend a steering section 24 at the front or distal end of the insertion tube adjacent the viewing head 14.
	www.freepatentsonline.com /5052803.html (3709 words)

	Project-Team - LEAR
		Discriminative learning overfits, whereas in generative learning each model is unaware of the opposite class and hence fails to capture details necessary for discrimination.
		Generative models are more general, they extend more naturally to complex problems (missing data, multiple classes...), and they are often simpler to learn and stabler as the classes do not interact.
		But if overfitting can be avoided, discriminative models typically have better classification performance: they are optimized directly for this, they have no need to model details that are important for class description but irrelevant for inter-class discrimination, and in particular they are often remarkably insensitive to certain kinds of mismodelling of the classes.
	www.inria.fr /rapportsactivite/RA2003/lear/module9.html (483 words)

	[No title]
		However, all standard neural network architectures such as the fully connected multi-layer perceptron are prone to overfitting [10]: While the network seems to get better and better, i.e., the error on the training set decreases, at some point during training it actually begins to get worse again, i.e., the error on unseen examples increases.
		There are basically two ways to fight overfitting: reducing the number of dimensions of the parameter space or reducing the effective size of each dimension.
		Nevertheless, overfitting might sometimes go undetected because the validation set is finite and thus not perfectly representative of the problem.
	www.ubka.uni-karlsruhe.de /vvv/1998/informatik/5/5.text (4927 words)

	Neural network - Open Encyclopedia (Site not responding. Last check: 2007-10-21)
		The danger is that the network overfits the training data and fails to capture the true statistical process generating the data.
		The training of SVMs is based on quadratic programming, a form of optimization that (usually) has only one global minimum.
		Therefore, and because SVMs have means to reduce the danger of overfitting, some practitioners prefer SVM training to neural network training.
	open-encyclopedia.com /Neural_networks (2361 words)

	comp.ai.neural-nets FAQ, Part 3 of 7: Generalization (Site not responding. Last check: 2007-10-21)
		Overfitting is especially dangerous because it can easily lead to predictions that are far beyond the range of the training data with many of the common types of NNs.
		If you have at least 30 times as many training cases as there are weights in the network, you are unlikely to suffer from much overfitting, although you may get some slight overfitting no matter how large the training set is. For noise-free data, 5 times as many training cases as weights may be sufficient.
		Overfitting is not confined to NNs with hidden units.
	www.faqs.org /faqs/ai-faq/neural-nets/part3 (14017 words)

	BioMed Central \| Full text \| Contextual weighting for Support Vector Machines in literature mining: an application to ...
		To assess the performance in a more general setting and to avoid the overfitting effect of memorizing, we do not include the instance of the term to be disambiguated into its context, as the purpose of this paper is to study context-based name disambiguation.
		The weighted SVM performance with different combinations of γ and the penalty parameter C follows the behavior described by Keerthi and Lin [53]: Areas of underfitting can be seen at the left, where the value of the C parameter is low, and at bottom left where the values of both C and γ are low.
		Overfitting happens also with noisy data at the right part of the figure, where the value of C is too large.
	www.biomedcentral.com /1471-2105/6/157 (7849 words)

	Artificial neural network - Wikipedia, the free encyclopedia
		As a result, representational resources may be wasted on areas of the input space that are irrelevant to the learning task.
		A common solution is to associate each data point with its own centre, although this can make the linear system to be solved in the final layer rather large, and requires shrinkage techniques to avoid overfitting.
		Like Gaussian Processes, and unlike SVMs, RBF networks are typically trained in a Maximum Likelihood framework by maximizing the probability (minimizing the error) of the data under the model.
	en.wikipedia.org /wiki/Artificial_neural_network (4236 words)

	Applications of Data Mining Technology to Remote Sensing Imagery Classification
		Overfitting of the training data can make the decision trees fit the training data perfectly, but poorly fit other independent data.
		This constraint is meant to minimize overfitting of the data.
		Overfitting the data results in a classification model that is applicable only to the data set on which it was developed, and not to other data sets.
	www.agrenv.mcgill.ca /AGRENG/staff/PRASHER/pages/researchpage/class.htm (6501 words)

	4.4.4.7. How can I test whether all of the terms in the functional part of the model are necessary?
		The fact that the basic strategy for testing is similar to other uses of the lack-of-fit statistic means that this test can only be used if the data set includes replicate measurements, as explained elsewhere.
		The value of the cut-off is from a user-specified probability of wrongly rejecting a model that does not overfit the data.
		To use the lack-of-fit test to simultaneously test for missing or misspecified terms in the model and overfitting, the two "one-sided" tests described in the preceding paragraph and on the previous page should each be used with upper and lower cutoff values each with significance levels of
	www.6sigma.us /handbook/pmd/section4/pmd447.htm (1519 words)

	[No title] (Site not responding. Last check: 2007-10-21)
		For example, if e=.1 and n=80, then a training set of size 800 that is trained until 95% correct classification is achieved on the training set, should produce 90% correct classification on the testing set.
		Too much training "overfits" the data, and hence the error rate will go up on the testing set.
		Hence it is not usually advantageous to continue training until the MSE is minimized.
	csis.pace.edu /~benjamin/courses/cs627/webfiles/neural.nets/nn.15.html (269 words)

	Practice tennis ball and apparatus - Patent 4065126
		A knit cover overfits the sewn jacket and may receive a marking powder suitable to removably indicate a contact upon a surface when the ball is in play.
		The knit cover removably overfits the sewn jacket to facilitate insertion of additional applications of marking powder.
		The apparatus includes targets in form suitable for application upon a vertical surface to receive the soft, pliable ball as it is propelled by a tennis racket.
	www.freepatentsonline.com /4065126.html (2289 words)

	[No title]
		CART addresses the overfitting problem via tree pruning and error estimation algorithms that locate the "right sized" tree, ensuring a parsimonious, accurate classifier.
		These vectors, together with class priors and the cost function (these are optional), are input to the Tree Growing Module which then constructs the maximal tree (T~~) that character- izes the training data.
		Since this tree overfits the data, the next step is to construct a series of nested sub-trees by pruning Tmax to the root.
	trec.nist.gov /pubs/trec1/papers/17.txt (3438 words)

	Simple Heuristics That Make Us Smart
		General strategies that can be made to conform to a broad range of environments, on the other hand, can end up being too highly focused to be of much real use--having a large number of free parameters to fiddle with can be a hindrance.
		This failure of generalization, a phenomenon known as overfitting (e.g., Geman et al., 1992; Massaro, 1988), stems from assuming that every detail is of utmost relevance.
		But models of inference that try to be like a Laplacean superintelligence are doomed to overfitting, when they swallow more data than they can digest.
	www.bbsonline.org /documents/a/00/00/04/69/bbs00000469-00/bbs.todd.html (14898 words)

	CSE 291 Lecture Notes, February 22, 2005
		xbar is the exact mean of the training data, but it is not the exact mean of the population, so using xbar instead of mu can be called overfitting.
		The alternative estimator with n under-estimates the true variance, so from a bias point of view, it overfits the training data also, and fits the population poorly.
		This contrast illustrates that the concept of overfitting, while very important intuitively, is tricky to make precise.
	www-cse.ucsd.edu /~elkan/291/feb22.html (710 words)

	ICSLP-2000 Abstract: Myrvoll et al. (Site not responding. Last check: 2007-10-21)
		Although it has been shown that this approach can yield good results when adaptation data is scarce, an inherent problem needs to be considered: the number of transformations used has a significant influence on the adaptation performance.
		Using too many transformations will result in poorly estimated transformation parameters, eventually leading to a model that overfits the adaptation data.
		On the other hand, when too few transformations are used, a restricted mapping is obtained, leading to a suboptimal adapted model.
	www.isca-speech.org /archive/icslp_2000/i00_4540.html (240 words)

	Eyedrop dispenser with eyelid opening means - Patent 4543096
		An eyedrop dispenser is disclosed which includes a plastic squeeze bottle having a dispensing nozzle at one end.
		A collar overfits the dispenser end of the bottle and carries a pair of cooperating, forwardly extending fingers for eyelid contacting purposes.
		The fingers terminate forwardly in respective eyelid contactors and one finger is pivotally movable relative to the other to spread the eyelid contactors during the eyedrop dispensing process.
	www.freepatentsonline.com /4543096.html (1945 words)

	[No title]
		The 10 node network, by contrast, is volatile and overfits the data with frequency.
		As the networks that feature graphs were initialized to zero, we think this reflects some strage joint overfitting that, at round 90, was discovered by all networks and reversed.
		Readjustments can be seen to occur shortly after round 90 and are reflected in both test and validation sets.
	www.cs.columbia.edu /~evs/ais/finalprojs/allinNN (914 words)

	SEDI5-4
		It has been suggested that these are associated with both the tangent cylinder from the inner core, and with thermal structure in the deep mantle.
		The historical studies in turn encouraged similar modelling with archeomagnetic and paleomagnetic data; similar flux patches can be seen in the time-averaged paleomagnetic field (although it has been argued that this overfits the data), and also sometimes (but not always) in the archeomagnetic field.
		Simple scaling arguments suggest that the influence of advection of the field by core flow dominates that of magnetic diffusion in the evolution of the field on decadal time scales.
	mahi.ucsd.edu /cathy/SEDI2002/ABST/SEDI5-4.html (1682 words)

	Performance of TurboProp 2
		A common myth that some people like to talk about is that all neural nets overfit.
		In this example we’ll show that Turboprop 2 nets are hard to overfit, as long as you don’t overload the net with too many inputs or give it too few bars in the "training" set.
		If the net overfits, it will produce a prediction that is more like the close than like the smooth moving average.
	www.neuroshell.com /products.asp?task=turboprop (975 words)

	Simple Heuristics That Make Us Smart
		In addition, by being simple, these heuristics can avoid being too closely matched to any particular environment—that is, they can escape the curse of overfitting, which often strikes more complex, parameter-laden models, as described next.
		General strategies that can be made to conform to a broad range of environments, on the other hand, can end up being too highly focused to be of much real use—having a large number of free parameters to fiddle with can be a hindrance.
		Fast and frugal heuristics can reduce overfitting by ignoring the noise inherent in many cues and looking instead for the "swamping forces" reflected in the most important cues.
	www-abc.mpib-berlin.mpg.de /users/ptodd/SimpleHeuristics.BBS (14832 words)

	PredictionWorks: Data Mining Glossary
		Overfitting: The act of mistaking noise in training data for true trends in the population.
		An overfitted model will make incorrect predictions in those regions it overfit.
		Decision Tree algorithms typically employ prunning to avoid overfitting.
	www.predictionworks.com /glossary/index.html (6432 words)

	Getting Started with the Curve Fitting Toolbox (Curve Fitting Toolbox)
		coefficients for the fifth degree polynomial suggest that it overfits the census data.
		Note The fitted coefficients associated with the constant, linear, and quadratic terms are nearly identical for each polynomial equation.
		However, as the polynomial degree increases, the coefficient bounds associated with the higher degree terms increase, which suggests overfitting.
	www.weizmann.ac.il /matlab/toolbox/curvefit/ch_star5.html (635 words)

	Model Selection (Site not responding. Last check: 2007-10-21)
		This is, the final model should explain a substantial protion of the total variation, including all genes which appear to have significant effect on the trait in question.
		However, there is a risk of overfitting by including genes of no, or negligible, effect.
		A model that overfits the data at hand, by including too many negligible genes, will be a poor predictor because it relies on genes with no information about the trait.
	www.stat.wisc.edu /~yandell/brazil/node4.html (569 words)

	Soft Margins for AdaBoost (ResearchIndex) (Site not responding. Last check: 2007-10-21)
		Abstract: Recently ensemble methods like AdaBoost were successfully applied to character recognition tasks, seemingly defying the problems of overfitting.
		This paper shows that although AdaBoost rarely overfits in the low noise regime it clearly does so for higher noise levels.
		Central for understanding this fact is the margin distribution and we find that AdaBoost achieves -- doing gradient descent in an error function with respect to the margin -- asymptotically a hard margin distribution, i.e.
	citeseer.ist.psu.edu /707919.html (515 words)

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