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Products & Solutions / By Solution / Neurointelligence / Feature Set
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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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