Machine Learning

Machines can learn from examples and make predictions in similar but unknown situations. Machine learning addresses statistics problems of regression, interpolation, density estimation and pattern recognition . The applications overlap with KDD applications and include financial predictions, medical diagnosis, process control, fraud detection, handwriting and speech recognition, and information retrieval in text databases.

How can a machine learn?

Learning machines usually refer to computer programs that learn from examples. Example of learning machines include artificial neural networks, decision trees, and Support Vector Machines. Such learning machines have tunable parameters that are adjusted using training examples to achieve a particular objective (e.g. classify correctly objects into a number of classes). After training, the learning machine is ready to make predictions on new unseen examples.

The right learning machine for the right application

Experience is needed to preprocess data to facilitate the learning task and select an appropriate learning machine. We have more than 15 years of experience in designing learning machine architectures and algorithms, particularly neural networks and kernel methods. We are co-inventor of the widely used support vector machine technique. We work closely with our customers to incorporate domain knowledge about the task in the architectural design and constraints on the parameters. We provide visualization aids and detailed reports to help understand the predictions made on new data.

Data is almost everything

Having good data is essential. We help our customers with their design of experiments to optimally train and test your learning machine and make them benefit from our experience in data collection, benchmarking, data management and data cleaning.


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Berkeley, CA 94708, USA
1+ (510) 524-6211
Email: info at clopinet.com