Learning from examples

Just like humans, ANNs learn from examples. The examples are delivered as input data. The learning process of an ANN is called training. In the human brain, the synaptic connections, and thus the connections between the neurons. 

Neural networks model the functionality of the brain. They learn from examples, whereby the weights of the neurons are adapted on the basis of training data. 

As it is often the best and easiest to learn from examples, this chapter contains two different case studies to illustrate the proper use and benefits of statistically designed experiments during process development work (performed prior to actual validation studies). In order to provide a template for performing a good DOE study, each case study will follow the same general sequence of steps as follows ... 

Artificial neural networks learn from examples. Once fed with representative data, they are able to model the relationships between the data. 

Pitas, L, Milios, E., and Venetsanopoulos, A.N., Minimum entropy approach to rule learning from examples, IEEE Trans. Syst. Man Cybern. SMC-22 621-635,1992. 

Learning from examples and practice consists of the extraction and refinement of knowledge from positive and negative examples or from practical experience. 

A neural network performs parallel and distributed information processing that is learned from examples, and can hence be used for complex bioimpedance signal processing. 

Figure 5. Rules definition by learning from examples.

We have already answered all the questions and will stop here. In addition, we have no data to do any further calculations. But remember, you can specify six variables, but they must be independent of each other. We learned from example 5 that you cannot arbitrarily fix the three flow streams. 

Question As we learned from Examples 5.2 and 5.3, for the active gas corrosion of Ti by HCl at T = 1500 K the surface reaction rate and diffusion rate are approximately equal. Considering both processes operating in series, calculate the actual overall etching rate for Ti under this situation. 

Pirolli, P. L., Anderson, J. R. The role of learning from examples in the acquisition of recursive propamming skills. Canadian Journal of Psychology, 39,240-272.1985. 

It should be noted that Definition 3-1 is not the only possible definition for specifications by examples. It is too much geared towards the synthesis of algorithms. So let s generalize it to the larger framework of empirical learning from examples. Specification approaches vary according to the following criteria ... 

All these criteria allow a precise classification of settings for learning from examples. 

In Section 3.2.1, we suggest a terminology for the components of an empirical learning system. Then, in Section 3.2.2, we define rules of inductive inference, and survey their usage in empirical learning from examples in Section 3.2.3. Finally, in... 

Section 3.2.4, we define the niche of algorithm synthesis from examples within empirical learning from examples, and give pointers to the literature in Section 3.2.5. 

The synthesis of algorithms from examples is a machine learning task as it falls into the category of empirical learning from examples. Indeed, let s state the objectives of both fields ... 

But algorithm synthesis from examples is a highly specialized niche within empirical learning from examples. Table 3-1 summarizes the differences between the concerns of algorithm synthesis (as we view it) and the mainstream concerns (so far) of empirical learning. 

In Section 3.2.4, we have defined algorithm synthesis from examples as a niche of empirical learning from examples. As a reminder, for algorithm synthesis, we are here only interested in the setting with human specifiers who know (even if only informally) the intended relation, and who are assumed to choose only examples that are consistent with the intended relation. Moreover, the intended relation is assumed to have a recursive algorithm. There is a general consensus that a synthesizer from examples would be a useful component of any larger synthesis system. So we now draw some conclusions about the approaches surveyed in the previous two sections. 

Similarly, note that properties are totally different from background knowledge, as often used in concept-learning from examples. Indeed, properties partially define the predicate(s) that is (are) incompletely specified by the examples, whereas background information defines predicate(s) that is (are) different from the one(s) found in the examples. 

---