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speci ed by the VC-dimension [152, 732] Cohn and Tesauro [152] show that for experiments conducted, the generalization error decreases exponentially with the number of examples, rather than the 1/PT result of the VC bound Experimental results by Lange and M nner [502] show that more training examples do not necessarily improve a generalization In their paper, Lange and M nner introduce the notion of a critical a training set size Through experimentation they found that examples beyond this critical size do not improve generalization, illustrating that excess patterns have no real gain This critical training set size is problem dependent While enough information is crucial to e ective learning, too large training set sizes may be of disadvantage to generalization performance and training time [503, 948] Redundant training examples may be from uninteresting parts of input space, and do not serve to re ne learned weights it only introduces unnecessary computations, thus increasing training time Furthermore, redundant examples might not be equally distributed, thereby biasing the learner The ideal, then, is to implement structures to make optimal use of available training data That is, to select only informative examples for training, or to present examples in a way to maximize the decrease in training and generalization error To this extent, active learning algorithms have been developed Cohn et al [151] de ne active learning (also referred to in the literature as example selection, sequential learning, query-based learning) as any form of learning in which the learning algorithm has some control over what part of the input space it receives information from An active learning strategy allows the learner to dynamically select training examples, during training, from a candidate training set as received from the teacher (supervisor) The learner capitalizes on current attained knowledge to select examples from the candidate training set that are most likely to solve the problem, or that will lead to a maximum decrease in error Rather than passively accepting training examples from the teacher, the network is allowed to use its current knowledge about the problem to have some deterministic control over which training examples to accept, and to guide the search for informative patterns By adding this functionality to a NN the network changes from a passive learner to an active learner Figure 77 illustrates the di erence between active learning and passive learning With careful dynamic selection of training examples, shorter training times and better generalization may be obtained Provided that the added complexity of the example selection method does not exceed the reduction in training computations (due to a reduction in the number of training patterns), training time will be reduced [399, 822, 948] Generalization can potentially be improved, provided that selected examples contain enough information to learn the task Cohn [153] and Cohn et al [151] show through average case analysis that the expected generalization performance of active learning is signi cantly better than passive learning Seung et al [777], Sung and Niyogi [822] and Zhang [948] report similar improvements Results presented by Seung et al indicate that generalization error decreases more rapidly for active learning than for passive learning [777] Two main approaches to active learning can be identi ed, ie incremental learning.

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A design pattern describes a good solution to a recurring problem in a particular context The pattern follows a prescribed format that includes the pattern name, a description of the context, the forces (goals and constraints), and the solution The idea is to record the experience of experts in a way that can be used by others facing a similar problem In addition to the solution itself, the name of the pattern is important and can form the basis for a domain specific vocabulary that can significantly enhance communication between designers in the same area Design patterns were first proposed by Christopher Alexander The domain was city planning and architecture [AIS77] Design patterns were originally introduced to the software engineering community by Beck and Cunningham [BC87] and became prominent in the area of object oriented programming with the publication of the book by Gamma, Helm, Johnson, and Vlissides [GHJV95], affectionately known as the GoF (Gang of Four) book This book gives a large collection of design patterns for object oriented programming To give one example, the Visitor pattern describes a way to structure classes so that the code implementing a heterogeneous data structure can be kept separate from the code to traverse it Thus, what happens in a traversal depends on both the type of each node and the class that implements the traversal This allows multiple functionality for data structure traversals, and significant flexibility as new functionality can be dded without having to change the data structure class The patterns in the GoF book have entered the lexicon of object oriented programming references to its patterns are found in the academic literature, trade publications, and system documentation These patterns have by now become part of the expected knowledge of any competent software engineer An educational nonprofit organization called the Hillside Group [Hil] was formed in 1993 to promote the use of patterns and pattern languages and, more generally, to improve human communication about computers "by encouraging people to codify common programming and design practice" To develop new patterns and help pattern writers hone their skills, the Hillside Group sponsors an annual Pattern Languages of Programs (PLoP) workshop and several spinoffs in other parts of the world, such as ChiliPLoP (in the western United States), KoalaPLoP (Australia), EuroPLoP (Europe), and .

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