

Bibliographical RemarksIn the first section of this chapter we introduced the Bayesian inference principle whose basis is given by Bayes' theorem (see equation (3.1)). Excellent monographs introducing this principle in more detail are by Bernardo and Smith (1994) and by Robert (1994); for a more applied treatment of ideas to the problem of learning see MacKay (1991) and MacKay (1999). It was mentioned that the philosophy underlying Bayesian inference is based on the notion of belief. The link between belief and probability is established in the seminal paper Cox (1946) where a minimal number of axioms regarding belief are given. Broadly speaking, these axioms formalize rational behavior on the basis of belief. A major concept in Bayesian analysis is the concept of prior belief. In the book we have only introduced the idea of conjugate priors. As the prior is the crux of Bayesian inference there exist, of course, many different approaches to defining a prior, for example on the basis of invariances w.r.t. parameterization of the likelihood (Jeffreys 1946, Jaynes 1968). In the context of learning, the model selection principle of evidence maximization was formulated for the first time in MacKay (1992). In Subsection 3.1.1 we introduced several prediction strategies on the basis of posterior belief in hypotheses. Note that the term Bayes classification strategy (see Definition 3.7) should not be confused with the term Bayes (optimal) classifier which is used to denote the strategy which decides on the class y that incurs minimal loss on the prediction of x (see Devroye et al. (1996)). The latter strategy is based on complete knowledge of the data distribution P_{Z} and therefore achieves minimal error (sometimes also called Bayes error) for a particular learning problem. Section 3.2 introduced Bayesian linear regression (see Box and Tiao (1973)) and revealed its relation to certain stochastic processes known as Gaussian processes (Feller 1966); the presentation closely follows MacKay (1998), Williams (1998). In order to relate this algorithm to neural networks (see Bishop(1995)) it was shown in Neal (1996) that a Gaussian process on the targets emerges in the limiting case of an infinite number of hidden neurons and Gaussian priors on the individual weights. The extension to classification using the Laplace approximation was done for the first time in Barber and Williams (1997), Williams and Barber (1998). It was noted that there also exists a Markov chain approximation (see Neal (1997b)) and an approximation known as the mean field approximation (see Opper and Winther (2000)). It should be noted that Gaussian processes for regression estimation are far from new; historical details dating back to 1880 can be found in Lauritzen (1981). Within the geostatistics field, Matheron proposed a framework of regression identical to Gaussian processes which he called "kriging" after D. G. Krige, a South African mining engineer Matheron (1963). However, the geostatistics approach has concentrated mainly on lowdimensional problems. The algorithmical problem of inverting the Gram matrix has been investigated by Gibbs and MacKay (1997) who also proposes a variational approximation to Gaussian processes; for other approaches to speeding Gaussian process regression and classification see Trecate et al. (1999), Williams and Seeger (2001) and Smola and Bartlett (2001). Finally, the reasoning in Remark 3.13 is mainly taken from Sollich (2000). The relevance vector machine algorithm presented in Section 3.3 can be found in Tipping (2000) and Tipping (2001). This algorithm is motivated by automatic relevance determination (ARD) priors which have been suggested in MacKay (1994) and Neal (1996) and empirically investigated in Neal (1998). There exists a variational approximation to this method found in Bishop and Tipping (2000). In Section 3.4 we presented the Bayes point machine which is also known as the optimal perceptron (Watkin 1993). This algorithm has received a lot of attention in the statistical mechanics community (Opper et al. 1990, Opper and Haussler 1991, Biehl and Opper 1995, Opper and Kinzel 1995, Dietrich et al. 2000). There it has been shown that the optimal perceptron is the classifier which achieves best generalization error on average and in the socalled thermodynamical limit, i.e., the number of features n and the number samples m tend to infinity although their ratio m/n=b stays constant. The idea of using a billiard on the unit hypersphere is due to Ruján (1997); "kernelization'' was done independently by Ruján and Marchand (2000) and Herbrich et al. (2001). For an extensive overview of other applications of Markov Chain Monte Carlo methods the interested reader is referred to Neal (1997a). There exist several extension to this algorithm which aim to reduce the computational complexity (see Herbrich and Graepel (2001a) and Rychetsky et al. (2000)). A promising approach has been presented in Minka (2001) where the uniform posterior measure over version space is approximated by a multidimensional Gaussian measure. This work also presents a modification of the billiard algorithm which is guaranteed to converge Minka (2001, Section 5.8). The algorithm presented in the last section, that is, Fisher linear discriminants, has its roots in the first half of the last century Fisher (1936). It became part of the standard toolbox for classification learning (also called discriminant analysis when considered from a purely statistical perspective). The most appealing feature of Fisher discriminants is that the direction vector found is the maximizer of a function which approximately measures the interclass distance vs.~the innerclass distance after projection. The difficulty in determining this maximizer in general has been noticed in several places, e.g., Vapnik (1982, p. 48). The idea of kernelizing this algorithm has been considered by several researchers independently yet at the same time (see Baudat and Anouar (2000), Mika et al. (1999) and Roth and Steinhage (2000)). Finally, the equivalence of Fisher discriminants and least squares regression, demonstrated in Remark 3.16, can also be found in Duda et al. (2001). It is worth mentioning that, beside the four algorithms presented, an interesting and conceptually different learning approach has been put forward in Jaakkola et al. (2000) and Jebara and Jaakkola (2000). The algorithm presented there employs the principle of maximum entropy (see Levin and Tribus (1978)). Rather than specifying a prior distribution over hypotheses together with a likelihood model P_{ZH=h } for the objects and classes, given a hypothesis h, which, by Bayes' theorem, result in the Bayesian posterior, we consider any measure P_{H} which satisfies certain constraints on the given training sample z as a potential candidate for the posterior belief. The principle then chooses the measure P_{H}^{ME} which maximizes the entropy E_{H}[ln(P_{H}(H))]. The idea behind this principle is to use as little prior knowledge or information as possible in the construction of P_{H}^{ME}. Implementing this formal principle for the special case of linear classifiers results in an algorithm very similar to the support vector algorithm (see Section 2.4). The essential difference is given by the choice of the cost function on the margin slack variables. A similar observation has already been made in Remark 3.13. 