Chapter 1: Introduction

Table of contents

1.2.1. Versions
1.2.2. System requirements

1.3. Release notes

1.1. This manual ↩

This manual assumes basic knowledge of pattern recognition and Matlab environment. In order to embed trained classifiers into custom applications, basic familiarity with C language is also assumed.

The manual is structured in four parts:

User's guide - explains software functionality
Reference manuals for the PRSD Toolbox and for libPRSD C library describe the programing interface
Knowledge base - collects number of step-by-step usage examples and "howtos"
Glossary - explains basic pattern recognition terminology

1.2. Introduction to PRSD Studio ↩

PRSD Studio is a software toolkit simplifying design and deployment of pattern recognition algorithms. It consists of two components, the Matlab-based PRSD Toolbox facilitating algorithm design and the libPRSD library delivering the execution of trained classifiers to custom applications.

PRSD Studio provides tools for:

Construction of data sets
Handling of multiple sets of labels and arbitrary meta-data
Interactive visualization of data and meta-data
Training statistical detectors and discriminants
Quick evaluation of classifiers
Optimizing classifier decisions according to performance requirements using two-class and multi-class ROC analysis
Building hierarchies of classifiers
Deploying trained classifiers in custom applications out of Matlab

1.2.1. Versions ↩

PRSD Studio comes in three versions:

Lite: Free limited version for non-commercial use intended for people who are learning about pattern recognition. It contains only PRSD Toolbox and is limited to data sets with maximum 300 samples and three classes.
Academic: Full version of PRSD Studio discounted for use by university students and researchers for non-commercial projects only. The Academic version is bound a hardware dongle and includes both the PRSD Toolbox and the libPRSD library for execution of trained classifiers out of Matlab.
Commercial: Full version of PRSD Studio for commercial use. It includes personal license of PRSD Toolbox bound to a hardware dongle and unlimited royalty-free deployment of trained classifiers in custom commercial applications using libPRSD library.

For Academic and Commercial versions, also group licensing is available using floating licenses provided by a license server.

1.2.2. System requirements ↩

PRSD Studio is supported on the following platforms:

MS Windows 32-bit
MS Windows 64-bit
GNU Linux 32-bit (x86)
GNU Linux 64-bit (x86)
Apple Mac OS X (x86)

PRSD Studio requires Matlab 7.x. Limited functionality depends on PRTools 4.1.

1.3. Release notes ↩

development version 2.0.9 (8-Mar-2010)

adding support for subset by logical array for sddata and sdlab objects (example: a( a.lab=='banana' ))
sdtest raises a warning if some of the true classes are not matched to classifier decisions (all samples from these classes are considered misclassified)
fixed sdscatter problem with the order of classes in "class on top" and "change markers"
usability improvements in sdfeatplot (click to change figure title; legend properly displaying special characters)
'mean-error' performance measure may specify optional class priors used for weighting the class errors
global display verbosity may be handled using prsd_display command (use prsd_display off to switch off display output of PRSD Studio functions).
'nodisplay' options added in sdmixture, sdparzen, sdcrossval
randsubset supports random selection of objects from some classes only (example: [tr,ts]=randsubset(a,[0.5 0]) returns 50% of the first class for the training)
sdcrossval outputs string with the result summary, result struct and the evaluation object.

development version 2.0.8 (19-Feb-2010)

fix in sdimage for multi-dimensional images (image cubes)
pipelines now provide operating points via p.ops field
API interface simplification and cleanup
low-level output of pipelines on matrices and using C API returns indices to decision list as decisions, not the internal codes
sdlist and sdlab internal numerical representation is not exposed to the user anymore
feature selection pipelie sdp_fsel now may get the feature labels directly from the data set pf=sdp_fsel(data,[3 4])
sddetector handles output polarity automatically (k-NN output is distance, mixture output is similarity)
adding easy display of sdlab object details (class sizes, fractions) using the transpose operator (lab')

development version 2.0.5 (22-Dec-2009)

classifier output visualization using sdscatter can now switch between different soft outputs interactively using cursor keys
added constrain method for easy application of ROC performance constraints
enhanced setcurop method to choose operating point minimizing or maximizing specific performance measure or setting op.point based on costs
new performance measure nconfmat - the entry in normalized confusion matrix
'target' and 'non-target' options in sddetector and sdroc setting the desired target/non-target names
setstate method in sdalg algorithm allows to call algorithm function directly (instead of using the multiplication operator)

development version 2.0.4 (14-Dec-2009)

sdrelab now allows to add string prefix to all classes in all labels present using 'add to all' option. This makes it easy to compare two data sets with multiple labelings (classes, patients, tissues).
adding sdscale command for data scaling

development version 2.0.3 (9-Dec-2009)

isclass method for quick check if certain classes are present (useful for custom algorithms)
sdnorm function adding normalization step to a trained pipeline (this construct a general discriminant)
sdlab fix for incorrect class size when initialized with a list and indices
adding initial version of auto-conversion for older-format @sdppl/sdppl and @sdops/sdops objects
fix for the inMathOverflow warning/error in sdtree training

development version 2.0.2 (4-Dec-2009)

new sdlab object simplifies handling of labels, decisions and indexed meta-data
new sddata object brings easy handling of sample meta-data
multiple sets of labels or meta-data in a dataset, unified access to sample properties
simple queries using multiple criteria (give me all samples labeled as "Cancer" from patient 1,2 and 5 using subset(a,'class','Cancer','patient',[1 2 5]))
access to classes is greatly simplified
sdroc handles classifier output polarity automatically (sdexe stores the output type in output_type data property)
user may change class markers. Data set remembers class markers. Scatter markers are stored in the 'marker' property inside the class list.
dissimilarity representation contains as feature properties all prototype sample properties
labels and decisions may be easily concatenated. This allows us to add new labels with brake-down of errors (confusion-matrix entries) in one command.
writing custom sdalg algorithms is significantly simplified

1.x Compatibility changes

sdppl objects use new internal format.
sderror replaced by sdtest

stable version 1.3 (30-Nov-2009)

fix in sdnmean classifier: now computing pooled diagonal covariance using class priors
adding missing parse_measures.p file
fixing p-code copatibility problem with Matlab 7.4

development version 1.2.5 (12-Oct-2009)

fixes in findprop for numerical properties
adding 'all' and 'nodisplay' options to sdrelab

development version 1.2.4 (13-Aug-2009)

sdtree implements training of decision tree classifier scalable to large number of samples (example)
fix in prsd_feedback correcting the problem with PRTools not on Matlab path

development version 1.2.3 (15-Jul-2009)

visualization: sdscatter provides more detailed information in sample inspector including all sample meta-data
sdrelab: adding prefix or suffix to all class names. (example)
sdrelab: renaming a single class by relative index
simpler installation: PRSD Studio Lite installation does not anymore require software activation
sdroc: support for reject option on classifiers with distance soft output (sdknn)
selprop,findprop support for set of property values defined by cell array

development version 1.2.2 (16-Jun-2009)

libPRSD: support for AdaBoost execution using decision tree as base classifiers
visualization: sdscatter allows interactive change of classifier parameters using slider (k in k-NN, smoothing in Parzen, number of base classifiers in AdaBoost)
visualization: sdimage may be connected to ROC plot and visualize decisions at different operating points in real-time
sdneural provides target option that allows one to approximate trained classifiers (example)
sdroc: fraction of all objects may be rejected by specifying fraction after reject option (read more)

development version 1.2.1 (27-May-2009)

sdnbayes implementing Naive Bayes classifier with automatic selection of number of histogram bins
sdroc now supports cost-based selection of operating point for two-class scenario (in addition to the existing multi-class cost-based optimization)
sddecide may be used in pipelines to define default operating point
sdp_affine can construct simple feature scaling pipelines

stable version 1.2 (19-May-2009)

sdmixture supports automatic estimation of number of components
sdneural implementing feed-forward neural network training
sdcrossval now supports untrained pipelines

development version 1.1.6 (9-May-2009)

sdparzen Parzen classifier implementing scalar and vector smoothing
sdknn k-th nearest neighbor classifier with for prototype selection and support for both detection and multi-class classification

development version 1.1.5 (1-May-2009)

libPRSD now supports loading pipelines also from a buffer using prsd_LoadPipelineFromBuffer (pipelines may be now stored in application resources or sent over network).
sdroc supports rejection both far away and close to the decision boundary using the reject option.
sdscatter: the figure title may be selected interactively by clicking on the title area
simplified selection us performance measures in sdroc

development version 1.1.4 (19-Mar-2009)

adding support for group licenses via license server
support for construction of arbitrary hierarchical classifiers using decision-level fusion and their execution through libPRSD
sddetector brings one-command construction of detectors based on arbitrary model (both in one-class setting specifying a threshold using fraction of rejected samples and in two-class setup using ROC analysis to fix the threshold minimizing mean error).
sddrawroc allows to save the current operating point into any relevant object (sdroc,sdops, pipelines, sddecide mappings, custom sdalg algorithms)
introducing sdmixture for training Gaussian mixture models (one- or multi-class, variable number of components per class, different stopping criteria (iterations or likelihood delta))
sdrelab allows to define classes by ~ (tilda) negation operator (e.g. turn all what is not not apple into "non-apple")
sdscatter allows the user to flip through order of classes (z-order) by + and - key-strokes
number of usability improvements in construction of pipelines and interaction with PRTools (sdroc and sdops objects can be now directly concatenated into pipelines; sdmap wraps pipelines for use in PRTools)
many improvements in confusion matrix estimation: sdconfmat
setprop now allows to quickly set property to a constant value. This makes it very easy to quickly tag a group of samples with a specific label.
sdconfmat can now add new labels with all confusion matrix combinations as a property. This can be used to quickly visualize different types of error directly in the feature-space
sdconfmat cosmetic fix: string confusion matrix scales nicely with long class names
new function selprop returning a subset of a dataset with given property values
significant improvements in scalability of sdroc to large datasets in speed and memory usage. Practical even for datasets with 100 000 samples and tens of thousands of operating points.
improved ROC optimizer brings better quality sets of operating points
sdconfmat can now estimate confusion matrices for sets of operating points from the soft outputs
sdexe can return numerical decision codes ('code' option). This is useful for low-level work with classifier outputs.
pipelines can return numerical decision codes using .* operator (e.g. dec=data.*p)
sdeaclust clustering can be now executed on new data. Scalable to very large datasets (images).

development version 1.1.3 (26-Jan-2009)

fix in sdscatter allowing to paint labels with legend switched on
fix in sdscatter retaining the type of numerical properties in a dataset saved back to workspace
sdscatter can now switch visibility of classes or groups on/off. That's helpful when inspecting large datasets with many overlapping sample groups (patients). See context menu in sdscatter Figure windows. Painting now applies only to visible samples.
initial support for hierarchical systems composed of multiple classifiers returning decisions (sdp_cascade)
- support for meta-classes and different features at each classifier node.
- ROC analysis for hierarchical systems
sdconfmat added
- the order of labels and decisions (lablists) can be fixed by the user
- sdconfmat can correctly handle situations where only some classes/decisions are present in the test set (given the full lablists)
- sdconfmat can return the string with a table
- support for normalization of confusion matrices
- lablists may be supplied as cell arrays of strings or string arrays
support for weight-based operating points with reject option (rejection both close to the boundary and distance-based)
sdroc automatically shows rejected fraction and all per-class TPrs
support for similarity-based nad distance-based classifier outputs
adding reject fraction estimate to sdroc
support for leave-one-out over a property (object, person, patient...)
fix for the bug where sdscatter made error when mouse pointer was moved too quickly over the new window

development version 1.1.2 (18-Nov-2008)

adding fast approximated k-NN see example in our blog
adding a k-centres classifier capable of both one-class classification and multi-class discrimination
feature selection algorithm sda_featsel now supports also backward feature selection

development version 1.1.1 (09-Nov-2008)

adding leave-one-out evaluation to sdcrossval
adding sdfeatsel: robust feature selection using internal cross-validation loop. It supports custom-made feature selection algorithms
two example algorithms added illustrating the use of feature selection during training (sda_featsel_example1) and in inner cross-validation loop based on sdfeatsel (sda_featsel_example2)

stable version 1.1 (04-Nov-2008)

fixing critical bug in 1.1 26-Oct-2008 related to problem with dongles
fixing the issues with one-sample test sets in ROC

development version 1.1 (26-Oct-2008)

sdscatter gets full support for GUI menus and class renaming
new sdimage command visualizing image stored in a dataset. Support for label paiting, class renaming, multiple sample groupings, connection to sdscatter
sdscatter support for interactive sample inspector (datasets with 1D data using bar plot or 2D images)
sddrawroc can now show confusion matrices at the cursor and at the selected operating point (if present i.e. if 'confmat' flag was specified in sdroc command)
sdexe now automatically converts sdalg algorithms into pipelines
sdstackgen now returns also a robust base classifier (mean fusion of per-fold trained base classifiers) as a second output
improved support for prtools classifiers with output conversion
fix: sdroc now stores confusion matrices in multi-class situations using 'confmat' flag
fix to scaling using affine projection. scalem is now supported for all affine scaling types

stable version 1.0 (15-Sep-2008)

added randomization cross-validation scheme sdcrossval(nmc,data,'method','random')
ROC object may be queried using short names of measurements r(:,'err(Cancer)')
activation support for commercial demos

development version 1.0 (02-Sep-2008)

fix: included missing sda_prtools wrapper
new feature: sdscatter now allows for user-defined titles (sample details moved to the figure title bar)