Archive for September 2007

IBM Joins OpenOffice.org Community

September 14, 2007

10 September 2007 — The OpenOffice.org community today announced that IBM will be joining the community to collaborate on the development of OpenOffice.org software. IBM will be making initial code contributions that it has been developing as part of its Lotus Notes product, including accessibility enhancements, and will be making ongoing contributions to the feature richness and code quality of OpenOffice.org. Besides working with the community on the free productivity suite’s software, IBM will also leverage OpenOffice.org technology in its products.

read the full press release at OpenOffice.org

Advertisements

Machine Learning Development with Perl

September 11, 2007

I just posted in PerlMonks a draft of a 45 minutes-long talk on Machine Learning Development with Perl. Here is an extract of that post:

————————————————–

Machine Learning Development with Perl

The development of machine learning applications can be seen as a three-phase process involving: preparation, modeling, and implementation (See Fig. 1).

As a developer, you have to move back and forth between phases until you get a satisfactory result.

Preparation

In the preparation phase, you work with your customer to define the problem. You proceed, then, to gather some data. After that, you analyze the data and do some cleaning if necessary and select the features you are going to use in the model. Based on the type of problem, you may decide what type of model you want to develop: a classifier, an estimator, or a clustering application.

Modeling

In the modeling phase, you do the model selection in case you did not do it in the preparation phase and then you do the development and finally you do the evaluation. Based on the results you get, you may decide to got back to the preparation phase and select other features, other cleaning method, or maybe other type of model.

Implementation

In the implementation phase, you simply implement your model. One important consideration is that your model should continue learning from new data. Sometimes, in machine learning, your model works well initially but when the data grow significantly then the model does not perform as well as before. This is why it is important to allow the model to continue learning as more data become available.

————————————————–

The full post ( including source code ) is available at RFC: Machine Learning Development with Perl

Cheers,

Lino

Presenting AranduCorp

September 10, 2007

AranduCorp is a consulting firm focused on helping organizations improve their business processes, marketing, and sales. AranduCorp offers training and consulting services on predictive analytics and will soon offer affordable predictive analytics software solutions for small and medium size businesses.

For more information visit:

AranduCorp’s Website

AranduCorp’s Blog