Statistica offers a wide selection of statistical and graphical output. You may select multiple models and ensembles. When possible Statistica will display any results generated in a comparative fashion e.
This feature is particularly useful for comparing various models trained on the same data set. All statistics are generated independently for the training, test, and validation samples or combinations of your choice.
Overall statistics calculated include mean network error, the confusion matrix for classification problems which summarizes correct and incorrect classification across all classes , and the correlation for regression problems - all automatically calculated. Kohonen networks include a Topological Map window, which enables you to visually inspect unit activations during data analysis. Skip to main content. By: Angela Waner. Last updated: pm Apr 03, Flag for Review.
Table of Contents 1 1. Introduction Neural networks are a predictive modeling technique that is capable of modeling extremely complex functions and data relationships. The other types of problems regression, classification, time series have three different options for creating neural networks: automated neural netwoks search ANS custom neural networks CNS subsampling random, bootstrap The ability to learn by examples is one of the many features of neural networks that enables the user to model data and establish accurate rules governing the underlying relationship between various data attributes.
For more information see Kohonen, ; Fausett, ; Haykin, ; Patterson, Neural networks have a remarkable ability to derive and extract meaning, rules, and trends from complicated, noisy, and imprecise data. They can be used to extract patterns and detect trends that are governed by complicated mathematical functions that are too difficult, if not impossible, to model using analytic or parametric techniques.
One of the abilities of neural networks is to accurately predict data that were not part of the training data set, a process known as generalization.
Given these characteristics and their broad applicability, neural networks are suitable for applications of real world problems in research and science, business, and industry.
Below are examples of areas where neural networks have been successfully applied:. Neural networks are also intuitively appealing, since many of its principals are based on crude and low-level models of biological neural information processing systems, which have led to the development of more intelligent computers systems that can be used in statistical and data analysis tasks.
The brain is principally composed of a very large number approximately ten billion of neurons, massively interconnected with several thousand interconnects per neuron. Each neuron is a specialized cell that can create, propagate, and receive electrochemical signals. Like any biological cell, the neuron has a body, a branching input structure called the dendrites, and a branching output structure known as the axon. The axons of one cell connect to the dendrites of another via a synapse. When a neuron is activated, it fires an electrochemical signal along the axon.
This signal crosses the synapses to thousands of other neurons, which may in turn fire, thus propagating the signal over the entire neural system i.
A neuron fires only if the total signal received at the cell body from the dendrites exceeds a certain level known as threshold. Although a single neuron accomplishes no meaningful single task on its own, when the efforts of a large number of them are combined, the results become quite dramatic for they can create or achieve various and extremely complex cognitive tasks such as learning and even consciousness. Thus, from a very large number of extremely simple processing units the brain manages to perform extremely complex tasks.
While there is a great deal of complexity in the brain that has not been discussed here, it is interesting that artificial neural networks can achieve some remarkable results using a basic model such as this. Schematic of a single neuron system. The inputs x send signals to the neuron at which point a weighted sum of the signals are obtained and further transformed using a mathematical function f.
Here we consider the simplest form of artificial neural networks with a single neuron with a number of inputs and one for the sake simplicity output. Although a more realistic artificial network typically consists of many more neurons, this model helps us to shed light on the basics of this technology. The neuron receives signals from many sources. The above architecture can be used for regression, classification, regression time series, classification time series, and cluster analysis.
In addition, ANS supports Ensembles networks formed from arbitrary when meaningful combinations of the network types listed above. Combining networks to form Ensemble predictions are particularly easy to use in SANN, especially for noisy or small datasets.
SANN contains numerous facilities to aid in selecting an appropriate network architecture. For data visualization, SANN can also display scatter-plots and 3D response surfaces to help the user understand the network's "behavior. SANN Automatically retains copies of the best networks, which can be retrieved at any time.
The usefulness and predictive validity of the network can Automatically be assessed by including test and validation samples and by evaluating the size and efficiency of the network as well as the cost of misclassification. SANN supports a number of network customization options. You can specify a linear output layer for networks used in but Not restricted to regression problems or Softmax activation functions for probability- estimation in classification problems.
Cross-entropy error functions, based on information-theory models, are also included, and there is a range of specialized activation functions, including Exponential, Tangent Hyperbolic, Logistic Sigmoid, and Sine functions for both hidden and output neurons.
0コメント