Data Mining Mehmed Kantardzic (good english books to read .txt) 📖

on the general philosophical principle known as Occam’s razor. According to this principle, limiting the model complexity is more important than using true assumptions with all details. We should seek simpler models over complex ones and optimize the trade-off between model complexity and the accuracy of the model’s description and fit to the training data set. Models that are too complex and fit the training data very well or too simple and fit the data poorly are both not good models because they often do not predict future data very well. Model complexity is usually controlled in accordance with Occam’s razor principle by a priori knowledge.

Summarizing SLT, in order to form a unique model of a system from finite data, any inductive-learning process requires the following:

1. A wide, flexible set of approximating functions f(X, w), w ∈ W, that can be linear or nonlinear in parameters w.

2. A priori knowledge (or assumptions) used to impose constraints on a potential solution. Usually such a priori knowledge orders the functions, explicitly or implicitly, according to some measure of their flexibility to fit the data. Ideally, the choice of a set of approximating functions reflects a priori knowledge about a system and its unknown dependencies.

3. An inductive principle, or method of inference, specifying what has to be done. It is a general prescription for combining a priori knowledge with available training data in order to produce an estimate of an unknown dependency.

4. A learning method, namely, a constructive, computational implementation of an inductive principle for a given class of approximating functions. There is a general belief that for learning methods with finite samples, the best performance is provided by a model of optimum complexity, which is selected based on the general principle known as Occam’s razor. According to this principle, we should seek simpler models over complex ones and optimize the model that is the trade-off between model complexity and the accuracy of fit to the training data.

4.3 TYPES OF LEARNING METHODS

There are two common types of the inductive-learning methods. They are known as

1. supervised learning (or learning with a teacher), and

2. unsupervised learning (or learning without a teacher).

Supervised learning is used to estimate an unknown dependency from known input–output samples. Classification and regression are common tasks supported by this type of inductive learning. Supervised learning assumes the existence of a teacher—fitness function or some other external method of estimating the proposed model. The term “supervised” denotes that the output values for training samples are known (i.e., provided by a “teacher”).

Figure 4.8a shows a block diagram that illustrates this form of learning. In conceptual terms, we may think of the teacher as having knowledge of the environment, with that knowledge being represented by a set of input–output examples. The environment with its characteristics and model is, however, unknown to the learning system. The parameters of the learning system are adjusted under the combined influence of the training samples and the error signal. The error signal is defined as the difference between the desired response and the actual response of the learning system. Knowledge of the environment available to the teacher is transferred to the learning system through the training samples, which adjust the parameters of the learning system. It is a closed-loop feedback system, but the unknown environment is not in the loop. As a performance measure for the system, we may think in terms of the mean-square error or the sum of squared errors over the training samples. This function may be visualized as a multidimensional error surface, with the free parameters of the learning system as coordinates. Any learning operation under supervision is represented as a movement of a point on the error surface. For the system to improve the performance over time and therefore learn from the teacher, the operating point on an error surface has to move down successively toward a minimum of the surface. The minimum point may be a local minimum or a global minimum. The basic characteristics of optimization methods such as stochastic approximation, iterative approach, and greedy optimization have been given in the previous section. An adequate set of input–output samples will move the operating point toward the minimum, and a supervised learning system will be able to perform such tasks as pattern classification and function approximation. Different techniques support this kind of learning, and some of them such as logistic regression, multilayered perceptron, and decision rules and trees will be explained in more detail in Chapters 5, 6, and 7.

Figure 4.8. Two main types of inductive learning. (a) Supervised learning; (b) unsupervised learning.

Under the unsupervised learning scheme, only samples with input values are given to a learning system, and there is no notion of the output during the learning process. Unsupervised learning eliminates the teacher and requires that the learner form and evaluate the model on its own. The goal of unsupervised learning is to discover “natural” structure in the input data. In biological systems, perception is a task learned via unsupervised techniques.

The simplified schema of unsupervised or self-organized learning, without an external teacher to oversee the learning process, is indicated in Figure 4.8b. The emphasis in this learning process is on a task-independent measure of the quality of representation that is learned by the system. The free parameters w of the learning system are optimized with respect to that measure. Once the system has become tuned to the regularities of the input data, it develops the ability to form internal representations for encoding features of the input examples. This representation can be global, applicable to the entire input data set. These results are obtained with methodologies such as cluster analysis or some artificial neural networks, explained in Chapters 6 and 9. On the other hand, learned representation for some learning tasks can only be local, applicable to the specific subsets of data from the environment; association rules are a typical example of an appropriate methodology. It has been explained in more detail in Chapter 8.

4.4 COMMON LEARNING TASKS

The generic