We present our approach to predicting the severity of user posts in a mental health forum. This system was developed to compete in the 2016 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. Our entry employs a meta-classifier which uses a set of of base classifiers constructed from lexical, syntactic and metadata features. These classifiers were generated for both the tar...

the problem of automatic signature recognition has received little attention incomparison with the problem of signature verification, despite its potentialapplications for many business processes and can be used effectively in paperlessoffice projects. this paper presents model-based off-line signature recognition withrotation invariant features. non-linear rotation of signature patterns is one...

Fingerprint is the commonly used biometric property in security, commerce and forensic application. One common problem in pattern recognition is lack of samples, only a few fingerprint samples from each individual are available for training a classifier. This paper proposes an approach of fingerprint retrieval based on Bayes classifier by combining the features of Gabor and Minutia and attempte...

The current state-of-the-art for acoustic language recognition is an i-vector classifier followed by a discriminatively-trained multiclass back-end. This paper presents a unified approach, where a Gaussian i-vector classifier is trained using Maximum Mutual Information (MMI) to directly optimize the multiclass calibration criterion, so that no separate back-end is needed. The system is extended...

We present an extension of the learning classifier system XCS in which classifier conditions are represented by RPN expressions and stack-based Genetic Programming is used to recombine and mutate classifiers. In contrast with other extensions of XCS involving tree-based Genetic Programming, the representation we apply here produces conditions that are linear programs, interpreted by a virtual s...

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to...