We have experts in Machine learning who have developed algorithms in equity trading, fraud detection, NLP, predictive coding in risk models , auto-ediscovery and environmental modeling applications.
Machine learning is an increasingly used computational tool within human-computer interaction research. It is gaining attention commercially and academically because of its ability to predict “uncertain situation” which are missed by typical Bayesian and Markov statistical predictive model. While most of the researchers currently utilize an iterative approach to refining classifier models and performance, we provide ensemble classification techniques as an alternative. These learning algorithms incorporate
multiple classifiers to decide uncertainty depending upon application of the client dataset. This brought a further essence in creating an interactive visualization system that presents a graphical view of understanding relative merits of various classifiers.
This will enable a user to quickly combine multiple classifiers working on multiple feature sets to produce an ensemble classifier with accuracy that approaches best reported performance classifying .
This ensemble classification techniques incorporate different classifiers including Support Vector Machine , Fuzzy Decision Tree , Naive Bayes classifier and Neural Network .
Along with it, we also take Inductive- Analytical approaches in learning for bringing the Domain theory fragrances in the algorithms developed by us. These enable a computer program to automatically analyze a large body of data and decide what information is the most relevant for that domain or particular problem. This crystallized information can then be used to help people make decision faster and more accurately. One of the central problems of the information age is dealing with the enormous explosion in the amount of raw information that is available. So, it is of utmost importance to device a mechanism, to extract knowledge based solely on evaluations from data. This knowledge is iteratively input for further knowledge discoveries. The concept can be used for the process of reverse engineering and is also a part of software mining where existing software can be studied to form models like entity-relationship diagrams. The existing software artifacts contain enormous business value, so the process is not only important from engineering, but also from the business point of view. Above from this we also device semi-supervised learning system using co-learning & self-learning techniques.
The following techniques are used
1.Attribute/Feature Selection.
2. Supervised/Semi-Supervised/Supervised Learning.
3. Clustering.
4. Classification.
5. Association Rules.
6. Filters.
7. Estimators.
Invention and application of methods of Machine Learning in practical problems provides a potentially far reaching development in Computer Science. These enable a computer program to automatically analyze a large body of data and decide what information is most relevant for that domain. This crystallized information can then be used to help people make decision faster and more accurately. One of the central problems of the information age is dealing with the enormous explosion in the amount of raw information that is available. Machine learning (ML) has the potential to probe through this mass of information and convert it into knowledge that people can use.
The main objectives :
Our Machine learning codes can work on SQL and NoSQL type of database. We have readily available code structure for Hadoop. Therefore, if you are planning to implement Machine learning /predictive coding in Hadoop, we may be able to offer you cheapest cost solution.