Промышленный лизинг Промышленный лизинг  Методички 

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can be implemented on a computer as well. The field has really taken off since the 1980s, when statisticians started to use them and understand them better.

A neural network consists of artificial neurons connected together. Each neuron mimics its biological counterpart, taking various inputs, combining them, and producing an output. Since digital neurons process numbers, the activation function characterizes the neuron. In most cases, this function takes the weighted sum of its inputs and applies an S-shaped function to it. The result is a node that sometimes behaves in a linear fashion, and sometimes behaves in a nonlinear fashion-an improvement over standard statistical techniques.

The most common network is the feed-forward network for predictive modeling. Although originally a breakthrough, the back propagation training method has been replaced by other methods, notably conjugate gradient. These networks can be used for both categorical and continuous inputs. However, neural networks learn best when input fields have been mapped to the range between -1 and +1. This is a guideline to help train the network. Neural networks still work when a small amount of data falls outside the range and for more limited ranges, such as 0 to 1.

Neural networks do have several drawbacks. First, they work best when there are only a few input variables, and the technique itself does not help choose which variables to use. Variable selection is an issue. Other techniques, such as decision trees can come to the rescue. Also, when training a network, there is no guarantee that the resulting set of weights is optimal. To increase confidence in the result, build several networks and take the best one.

Perhaps the biggest problem, though, is that a neural network cannot explain what it is doing. Decision trees are popular because they can provide a list of rules. There is no way to get an accurate set of rules from a neural network. A neural network is explained by its weights, and a very complicated mathematical formula. Unfortunately, making sense of this is beyond our human powers of comprehension.

Variations on neural networks, such as self-organizing maps, extend the technology to undirected clustering. Overall neural networks are very powerful and can produce good models; they just cant tell us how they do it.





CHAPTER

Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering

You hear someone speak and immediately guess that she is from Australia. Why? Because her accent reminds you of other Australians you have met. Or you try a new restaurant expecting to like it because a friend with good taste recommended it. Both cases are examples of decisions based on experience. When faced with new situations, human beings are guided by memories of similar situations they have experienced in the past. That is the basis for the data mining techniques introduced in this chapter.

Nearest neighbor techniques are based on the concept of similarity. Memory-based reasoning (MBR) results are based on analogous situations in the past-much like deciding that a new friend is Australian based on past examples of Australian accents. Collaborative filtering adds more information, using not just the similarities among neighbors, but also their preferences. The restaurant recommendation is an example of collaborative filtering.

Central to all these techniques is the idea of similarity. What really makes situations in the past similar to a new situation? Along with finding the similar records from the past, there is the challenge of combining the information from the neighbors. These are the two key concepts for nearest neighbor approaches.

This chapter begins with an introduction to MBR and an explanation of how it works. Since measures of distance and similarity are important to nearest neighbor techniques, there is a section on distance metrics, including a discussion of the meaning of distance for data types, such as free text, that have no



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