release_notes.txt

模式识别工具箱。非常丰富的底层函数和常见的统计识别工具

PRTools 4.1 release notes

This is section supplies some  information about changes in 
PRTools4.1 with respect to PRTools4.0 A number of new 
possibilities has been created important for the handling of 
large datasets, multiple labels of objects, the optimisation of 
complexity and regularisation parameters and the handling of 
regression problems. 

1 Compatibility

Changes are generally upwards compatible. With a few exceptions 
old routines should still work. The main exception is that the 
undocumented feature of PRTools4.0 to obtain fields from dataset 
and mapping variables using the dot-construct (e.g. classnames = 
a.lablist) has been changed. From now on the official and 
guaranteed way to address fields is by using the get-commands 
(e.g. classnames = getlablist(a)). The reason is that for a number 
of fields subfields have been defined using structures, structure 
arrays and cell arrays. So users are  urged to use the get-and 
set-commands as also in future releases the constructions may 
change. PRTools still recognizes datasets contructed in the old 
way and automatically converts them.

2 Datafiles

A new object class, datafile, has bee created. The datafile class 
inherits most of the fields and methods of the dataset class, but 
extends them by allowing data that is not in core but stored in 
files on disk. As these may be large, handling of datafiles is 
restricted to administration, like desired sampling of objects 
and features and preprocessing (e.g. filtering and resizing of 
images). At some moment a datafile has to be converted into a 
dataset and it should fit then in the available memory. Datafiles 
are important to the extend PRTools possibilities with 
preprocessing and feature measurements within the same 
framework. Thereby classifiers may be designed and trained that 
can directly operate on raw images or other signals without the 
need to convert them first to datasets. For more information read  
datafiles help file.

3 Image processing routines

In relation with the above a large set of image processing 
routines operating on datafiles and datasets has been included. 
They are helpful to convert (sets of) images to features and 
datasets. A number of them assume that the dip_image toolbox is 
available.

4 Multiple labels

For some applications it is useful to have multiple labelings of 
the objects. For instance, pixels may be labeled according to the 
image region (grass, water, rock) as will as to the image 
category (mountains, seaside, city) as well as to some origin 
(France, England, Norway). A provision has been created to enable 
this. The various labelings and corresponding priors (and targets 
in case of soft labeling) are stored in the dataset, but just one 
of them is active and is accessed by getlablist, getlabels, 
getprior and getnlab. For more information read he multi_labeling 
help file.

5 Optimisation of complexity parameters and regularisation

Many trainable classifiers and mappings depend on some parameter 
controlling its complexity or regularisation. A general routine 
has been created to optimise such parameters by cross-validation. 
This is always done in a standard way: 20 steps of 5-fold cross-
validation. This increases the training time roughly by a factor 
100. The automatic optimisation is activated (for the routines 
for which it is implemented) by using a NaN in the function call. 
So w=ldc(a) uses no regularisation, w=ldc(a,1e-6) uses a user 
defined value of 1e-6 and w=ldc(a,NaN) activates the automatic 
optimisation. The actually  used parameter value may be retrieved 
afterwards by the routine getopt_pars. 

6 Regression

PRTools has already for a long time the possibility of datasets 
consisting of feature based vectors with one or more desired 
target values (the have the label type 抰argets