Notes 20110208 CIS 6050 Neural Networks

From SnOwy - Ed's Wiki Notebook

Speaker: Dr. David Calvert

Contents 1 Literature Review 2 Techniques Overview 3 Data Reduction 4 Clustering 5 Prediction

Literature Review

• Due 18th -- find ≥ 3 papers to review for uses of ANNs

Techniques Overview

• Sample data ...
  • Records = { 1, 2, 3 ... n }
  • Feature1 = { a11, a12, a13 ... a1n }
  • Feature2 = { a21, a22, a23 ... a2n }
  • Featurem = { am1, am2, am3 ... amn }
• n = number of samples
• m = number of features (measured per sample)
  • can be discrete or part of a series
• dimensionality of the data is m

Data Reduction

• possible to have too many features
  • network learns the wrong thing
  • network hones in on an incorrect pattern
  • unnecessary, meaningless complexity (noise)
  • transformations of other inputs
  • example: linear transformations -- F1; F2 = F1+1; F3 = F1/2;
  • example: random values -- F1; F2 = random();
• how do we know if values are actually needed in a problem?
  • use some data reduction to remove some columns from the dataset
  • mostly for columns which are transformations of other columns
• principle component analysis (PCA)
  • look for columns that are orthogonal to one another
  • looks for columns that are the most different
  • discards columns that are linear combinations
  • large variances may be interesting -- these are kept

Clustering

• grouping data based on similarity
• usually involves a distance between groups
• usually involves a centre for each group
• the idea -- finding the groups provides us with some knowledge of the data
• later elements can be compared to clusters to identify for which they are most similar
• can label the clusters to provide meaning
• providing a label is classification (providing meaning = classifying)
  • only if cluster actually represents something
• clustering requires at least one parameter
  • minimum distance
  • density
  • number of clusters
• clusters are seldom separable -- often overlap
  • classification
• labelling data
  • build a model which labels records
• once trained, the system can be find a label for previously unseen data
• normally requires some labelled data which is used to build the model
• a kind of classification where we attack labels to the classifications

Prediction

sequential or temporal

• normally with a sequence, we want to know what comes next
• sometimes more interested in modelling characteristics of the sequence without prediction
• identifies dynamics of the system
• example -- heart rhythm -- does an ECG trace period have a meaning for cardiac health?