Exam 6: Techniques for Predictive Modeling

In developing an artificial neural network, all of the following are important reasons to pre-select the network architecture and learning method EXCEPT

In a neural network, groups of neurons can be organized in a number of different ways; these various network patterns are referred to as ________.

Compared to the human brain, artificial neural networks have many more neurons.

In the mathematical formulation of SVM's, the normalization and/or scaling are important steps to guard against variables/attributes with ________ that might otherwise dominate the classification formulae.

Neural networks are called "black boxes" due to the lack of ability to explain their reasoning.

Though useful in business applications, neural networks are a rough, inexact model of how the brain works, not a precise replica.

________ is the most widely used supervised learning algorithm in neural computing.

With a neural network, outputs are attributes of the problem while inputs are potential solutions to the problem.

Writing the SVM classification rule in its dual form reveals that classification is only a function of the ________, i.e., the training data that lie on the margin.

What are the three steps in the process-based approach to the use of support vector machines (SVMs)?

Kohonen's ________ feature maps provide a way to represent multidimensional data in much lower dimensional spaces, usually one or two dimensions.

Historically, the development of ANNs followed a heuristic path, with applications and extensive experimentation preceding theory. In contrast to ANNs, the development of SVMs involved sound ________ theory first, then implementation and experiments.

In the opening vignette, which method was the best in both accuracy of predicted outcomes and sensitivity?

The network topology that allows only one-way links between layers, with no feedback linkage permitted, is known as backpropagation.

All the following statements about hidden layers in artificial neural networks are true EXCEPT

The student retention case study shows that, given sufficient data with the proper variables, data mining techniques are capable of predicting freshman student attrition with approximately ________ percent accuracy.

In machine learning, the ________ is a method for converting a linear classifier algorithm into a nonlinear one by using a nonlinear function to map the original observations into a higher-dimensional space.

The k-nearest neighbor algorithm is overly complex when compared to artificial neural networks and support vector machines.

In 1992, Boser, Guyon, and Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. How does the resulting algorithm differ from the original optimal hyperplane algorithm proposed by Vladimir Vapnik in 1963?

In the Coors case study, why was a genetic algorithm paired with neural networks in the prediction of beer flavors?