barcode printing using vb.net INSTANCE-BASED LEARNING in Software

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CHAPTER 8 INSTANCE-BASED LEARNING
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must choose its approximation before the queries are observed The eager learner must therefore commit to a single linear function hypothesis that covers the entire instance space and all future queries The lazy method effectively uses a richer hypothesis space because it uses many different local linear functions to form its implicit global approximation to the target function Note this same situation holds for other learners and hypothesis spaces as well A lazy version of BACKPROPAGATION, for example, could learn a different neural network for each distinct query point, compared to the eager version of BACKPROPAGATION discussed in 4 The key point in the above paragraph is that a lazy learner has the option of (implicitly) representing the target function by a combination of many local approximations, whereas an eager learner must commit at training time to a single global approximation The distinction between eager and lazy learning is thus related to the distinction between global and local approximations to the target function Can we create eager methods that use multiple local approximations to achieve the same effects as lazy local methods Radial basis function networks can be seen as one attempt to achieve this The RBF learning methods we discussed are eager methods that commit to a global approximation to the target function at training time However, an RBF network represents this global function as a linear combination of multiple local kernel functions Nevertheless, because RBF learning methods must commit to the hypothesis before the query point is known, the local approximations they create are not specifically targeted to the query point to the same degree as in a lazy learning method Instead, RBF networks are built eagerly from local approximations centered around the training examples, or around clusters of training examples, but not around the unknown future query points To summarize, lazy methods have the option of selecting a different hypothesis or local approximation to the target function for each query instance Eager methods using the same hypothesis space are more restricted because they must commit to a single hypothesis that covers the entire instance space Eager methods can, of course, employ hypothesis spaces that combine multiple local approximations, as in RBF networks However, even these combined local approximations do not give eager methods the full ability of lazy methods to customize to unknown future query instances
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8 7 SUMMARY AND FURTHER READING
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The main points of this chapter include: Instance-based learning methods differ from other approaches to function approximation because they delay processing of training examples until they must label a new query instance As a result, they need not form an explicit hypothesis of the entire target function over the entire instance space, independent of the query instance Instead, they may form a different local approximation to the target function for each query instance
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MACHINE LEARNING
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Advantages of instance-based methods include the ability to model complex target functions by a collection of less complex local approximations and the fact that information present in the training examples is never lost (because the examples themselves are stored explicitly) The main practical difficulties include efficiency of labeling new instances (all processing is done at query time rather than in advance), difficulties in determining an appropriate distance metric for retrieving "related" instances (especially when examples are represented by complex symbolic descriptions), and the negative impact of irrelevant features on the distance metric k-NEAREST NEIGHBOR an instance-based algorithm for approximating realis valued or discrete-valued target functions, assuming instances correspond to points in an n-dimensional Euclidean space The target function value for a new query is estimated from the known values of the k nearest training examples Locally weighted regression methods are a generalization of k-NEAREST NEIGHBOR which an explicit local approximation to the target function in is constructed for each query instance The local approximation to the target function may be based on a variety of functional forms such as constant, linear, or quadratic functions or on spatially localized kernel functions Radial basis function (RBF) networks are a type of artificial neural network constructed from spatially localized kernel functions These can be seen as a blend of instance-based approaches (spatially localized influence of each kernel function) and neural network approaches (a global approximation to the target function is formed at training time rather than a local approximation at query time) Radial basis function networks have been used successfully in applications such as interpreting visual scenes, in which the assumption of spatially local influences is well-justified Case-based reasoning is an instance-based approach in which instances are represented by complex logical descriptions rather than points in a Euclidean space Given these complex symbolic descriptions of instances, a rich variety of methods have been proposed for mapping from the training examples to target function values for new instances Case-based reasoning methods have been used in applications such as modeling legal reasoning and for guiding searches in complex manufacturing and transportation planning problems
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The k-NEAREST NEIGHBOR algorithm is one of the most thoroughly analyzed algorithms in machine learning, due in part to its age and in part to its simplicity Cover and Hart (1967) present early theoretical results, and Duda and Hart (1973) provide a good overview Bishop (1995) provides a discussion of k-NEAREST NEIGHBOR its relation to estimating probability densities An excellent current and survey of methods for locally weighted regression is given by Atkeson et al (1997) The application of these methods to robot control is surveyed by Atkeson et al (1997b)
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A thorough discussion of radial basis functions is provided by Bishop (1995) Other treatments are given by Powell (1987) and Poggio and Girosi (1990) See Section 612 of this book for a discussion of the EM algorithm and its application to selecting the means of a mixture of Gaussians Kolodner (1993) provides a general introduction to case-based reasoning Other general surveys and collections describing recent research are given by Aamodt et al (1994), Aha et al (1991), Haton et al (1995), Riesbeck and Schank (1989), Schank et al (1994), Veloso and Aamodt (1995), Watson (1995), and Wess et al (1994)
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