Data Mining by Mehmed Kantardzic (inspirational novels TXT) 📗

4.2 SLT

SLT is relatively new, but it is perhaps one of the best currently available formalized theories for finite-sample inductive learning. It is also known as the Vapnik-Chervonenkis (VC) theory. It rigorously defines all the relevant concepts for inductive learning and provides mathematical proofs for most inductive-learning results. In contrast, other approaches such as neural networks, Bayesian inference, and decision rules are more engineering-oriented, with an emphasis on practical implementation without needing strong theoretical proofs and formalizations.

SLT effectively describes statistical estimation with small samples. It explicitly takes into account the sample size and provides quantitative description of the trade-off between the complexity of the model and the available information. The theory includes, as a special case, classical statistical methods developed for large samples. Understanding SLT is necessary for designing sound, constructive methods of inductive learning. Many nonlinear learning procedures recently developed in neural networks, artificial intelligence, data mining, and statistics can be understood and interpreted in terms of general SLT principles. Even though SLT is quite general, it was originally developed for pattern recognition or classification problems. Therefore, the widely known, practical applications of the theory are mainly for classification tasks. There is growing empirical evidence, however, of successful application of the theory to other types of learning problems.

The goal of inductive learning is to estimate unknown dependencies in a class of approximating functions using available data. The optimal estimate corresponds to the minimum expected risk functional that includes general distribution of data. This distribution is unknown, and the only available information about distribution is the finite training sample. Therefore, the only possibility is to substitute an unknown true risk functional with its approximation given as empirical risk, which is computable based on the available data set. This approach is called ERM and it represents the basic inductive principle. Using the ERM inductive principle, one seeks to find a solution f(X, w*) that minimizes the empirical risk expressed through the training error as a substitute for the unknown true risk, which is a measure of the true error on the entire population. Depending on the chosen loss function and the chosen class of approximating functions, the ERM inductive principle can be implemented by a variety of methods defined in statistics, neural networks, automatic learning, and so on. The ERM inductive principle is typically used in a learning setting where the model is given or approximated first and then its parameters are estimated from the data. This approach works well only when the number of training samples is large relative to the prespecified model complexity, expressed through the number of free parameters.

A general property necessary for any inductive principle including ERM is asymptotic consistency, which is a requirement that the estimated model converge to the true model or the best possible estimation, as the number of training samples grows large. An important objective of the SLT is to formulate the conditions under which the ERM principle is consistent. The notion of consistency is illustrated in Figure 4.4. When the number of samples increases, empirical risk also increases while true, expected risk decreases. Both risks approach the common minimum value of the risk functional: min R(w) over the set of approximating functions, and for an extra large number of samples. If we take the classification problem as an example of inductive learning, the empirical risk corresponds to the probability of misclassification for the training data, and the expected risk is the probability of misclassification averaged over a large amount of data not included into a training set, and with unknown distribution.

Figure 4.4. Asymptotic consistency of the ERM.

Even though it can be intuitively expected that for n → ∞ the empirical risk converges to the true risk, this by itself does not imply the consistency property, which states that minimizing one risk for a given data set will also minimize the other risk. To ensure that the consistency of the ERM method is always valid and does not depend on the properties of the approximating functions, it is necessary that consistency requirement should hold for all approximating functions. This requirement is known as nontrivial consistency. From a practical point of view, conditions for consistency are at the same time prerequisites for a good generalization obtained with the realized model. Therefore, it is desirable to formulate conditions for convergence of risk functions in terms of the general properties of a set of the approximating functions.

Let us define the concept of a growth function G(n) as a function that is either linear or bounded by a logarithmic function of the number of samples n. Typical behavior of the growth function G(n) is given in Figure 4.5. Every approximating function that is in the form of the growth function G(n) will have a consistency property and potential for a good generalization under inductive learning, because empirical and true risk functions converge. The most important characteristic of the growth function G(n) is the concept of VC dimension. At a point n = h where the growth starts to slow down, it is a characteristic of a set of functions. If h is finite, then the G(n) function does not grow linearly for enough large training data sets, and it is bounded by a logarithmic function. If G(n) is only linear, then h → ∞, and no valid generalization through selected approximating functions is possible. The finiteness of h provides necessary and sufficient conditions for the quick convergence of risk functions, consistency of ERM, and potentially good generalization in the inductive-learning process. These requirements place analytic constraints on the ability of the learned model to generalize, expressed through the empirical risk. All theoretical results in the SLT use the VC dimension defined on the set of loss functions. But, it has also been proven that the VC dimension for theoretical loss functions is equal to the VC dimension for approximating functions in typical, inductive-learning tasks such as classification or regression.

Figure 4.5. Behavior of the growth function G(n).

The ERM inductive