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    Feature Set

    Key features

    • Create and apply neural networks to:
      • Forecasting
      • Classification
      • Function Approximation
      • Data Anomalies Detection
    • Analyze and preprocess datasets
    • Automatically search for the best neural network architecture
    • Analyze network performance with graphs and detailed statistics
    • Easy-to-use interface

    Analyze and Pre-process Your Data

    • Import Excel files
    • Import popular ASCII file formats (CSV, TXT, PRN)
    • Custom date formats and file structure definition
    • Input dataset size is limited only by the hardware of the computer

    • Date/Time values encoding
    • Categorical values encoding
    • Numeric values scaling
    • Min/max values specification for numeric columns scaling

    • Missing values handling for both numeric and categorical data
    • Outliers handling for numeric data
    • Automatic recognition of data entry errors (wrong type values)
    • Visual representation of data anomalies in the Dataset window

    • Automatic and manual column type identification (numeric, categorical, date, time, text)
    • Random, sequential and manual dataset partition onto training, validation and test sets
    • Accept/ignore records and columns manually

    • Statistical information for data columns
    • Binary columns for anomalies indication
    • Two methods of automatic lag columns insertion
    • Preprocessed data representation
    • Detailed Data Analysis and Data Preprocessing Reports

    Design Neural Network

    • Input feature selection (GA, stepwise, exhaustive).
    • Manual architecture specification (up to 5 hidden layers for multi-layer perceptron)
    • Heuristic architecture search with customizable range of search and sensitivity
    • Exhaustive architecture search
    • Customizable search range and search sensitivity
    • Detailed statistics for each tested architecture
    • Network fitness criteria: AIC, Test set error, Correlation, R-squared
    • Graphical representation of network fitness
    • Time-series networks
    • Network sets
    • Network visualization

    • Training algorithms: Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, Quasi-Newton, Quasi-Newton (Limited Memory), Incremental and Batch Back-Propagation
    • Automatic adjustment of learning rate and momentum for Back-Propagation algorithm
    • Activation functions: Linear, Logistic, Tanh, Softmax
    • Error functions: Sum-of-Squares, Cross-entropy
    • Classification model: Winner-takes-all, Confidence-limits (Accept/Reject levels)

    Control Network Training Process

    • Real-time training error graph
    • Real-time control on training parameters:
      • errors on training and validation set: MSE, MAE, CCR
      • error improvement
      • training speed (iterations per second)
      • # of iterations.
    • Continue training with new parameters
    • Jog weights
    • Add jitter

    • Correlation and r-squared real-time graphs
    • Error improvement graph
    • Weights distribution graph
    • Error distribution graph
    • Input importance graph
    • Training log: test and validation set error for each iteration

    • Early-stopping on generalization loss
    • Retain and restore best network
    • Stopping conditions:
      • target error on training and validation sets: MSE, MAE, CCR
      • error improvement: network error, dataset error
      • number of iterations
      • generalization loss
    • Automatic network retrains and selection of the best network among retrains
    • Retrains statistics
    • Weights initialization: manual randomization range; optimized for Uniform or Gaussian distribution

    Test and Analyze Performance

    • Actual vs Output graph
    • Scatter plot
    • Response graph
    • Confusion matrix
    • ROC curve
    • Actual vs Output Table with absolute and relative errors
    • Input importance graph

    Apply Network

    • Enter new cases manually or insert from the Clipboard
    • Load new cases from a new data file
    • Apply to selected records from your original dataset
    • Graphical network output representation
    • Output representation with Results Table
    • Confidence limits for network output
    • Save results in a separate file or copy them to the Clipboard

    General

    • Customizable interface
    • Detailed reporting
    • Online help system
    • Free technical support
    • Project files to keep all related information in one place
    • Sample financial, marketing, real estate and scientific problems included

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