Zero Output One Output

Ty Test Case

preferable? When asked to choose, most individuals find solution 2(c) most appealing. It is the only solution that preserves a sense of "rationality." It is the only one of the three that meets the smoothness criterion: if a point lies between two others, its value should be between the value of the points it is between. Solutions 2(a) and 2(b) violate this assumption. In a real problem, solutions 2(a) and 2(b) would not generalize as well as 2(c).

Tying this back to preprocessing, neural networks often find "lazy" solutions that fit the data, missing more fundamental relationships. In general, to the extent that the raw data can be transformed in ways that pull out and extract more subtle relationships, the model will have fewer inputs and typically perform better on a validation set.

Data preprocessing enhances subtle relationships and sifts through candidate variables to select a few synergistic inputs for a model. The net result is simpler models that generalize better.

Feature Extraction

The fundamental assumption underlying the application of neural and other modeling technologies applied to financial problems is that there is some predictable signal for making better than random decisions. One explanation for this is the "Financial Ponzi Scheme" hypothesis of Meir Statman.2 This hypothesis leads to a strategy for identifying potentially good transformations on data.

preferable? When asked to choose, most individuals find solution 2(c) most appealing. It is the only solution that preserves a sense of "rationality." It is the only one of the three that meets the smoothness criterion: if a point lies between two others, its value should be between the value of the points it is between. Solutions 2(a) and 2(b) violate this assumption. In a real problem, solutions 2(a) and 2(b) would not generalize as well as 2(c).

Tying this back to preprocessing, neural networks often find "lazy" solutions that fit the data, missing more fundamental relationships. In general, to the extent that the raw data can be transformed in ways that pull out and extract more subtle relationships, the model will have fewer inputs and typically perform better on a validation set.

Data preprocessing enhances subtle relationships and sifts through candidate variables to select a few synergistic inputs for a model. The net result is simpler models that generalize better.

feature Extraction

The fundamental assumption underlying the application of neural and other modeling technologies applied to financial problems is that there is some predictable signal for making better than random decisions. One explanation for this is the "Financial Ponzi Scheme" hypothesis of Meir Statman.2 This hypothesis leads to a strategy for identifying potentially good transformations on data.

Figure 16.1 Graphical depiction of data in Table 16.2. Network has

two inputs, x, and x2, each having three training values. This yields nine training combinations. There is also one test case.

Zero Output One Output

"1

T, Test Case

preferable? When asked to choose, most individuals find solution 2(c) most appealing. It is the only solution that preserves a sense of "rationality." It is the only one of the three that meets the smoothness criterion: if a point lies between two others, its value should be between the value of the points it is between. Solutions 2(a) and 2(b) violate this assumption. In a real problem, solutions 2(a) and 2(b) would not generalize as well as 2(c).

Tying this back to preprocessing, neural networks often find "lazy" solutions that fit the data, missing more fundamental relationships. In general, to the extent that the raw data can be transformed in ways that pull out and extract more subtle relationships, the model will have fewer inputs and typically perform better on a validation set.

Data preprocessing enhances subtle relationships and sifts through candidate variables to select a few synergistic inputs for a model. The net result is simpler models that generalize better.

Feature Extraction

The fundamental assumption underlying the application of neural and other modeling technologies applied to financial problems is that there is some predictable signal for making better than random decisions. One explanation for this is the "Financial Ponzi Scheme" hypothesis of Meir Statman.2 This hypothesis leads to a strategy for identifying potentially good transformations on data.