Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Communications of the ACM
A robust and scalable clustering algorithm for mixed type attributes in large database environment
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Clipping and Analyzing News Using Machine Learning Techniques
DS '01 Proceedings of the 4th International Conference on Discovery Science
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Authorship Attribution with Support Vector Machines
Applied Intelligence
Decision analysis of data mining project based on Bayesian risk
Expert Systems with Applications: An International Journal
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
A health social network recommender system
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Machine learning techniques such as support vector machines are applied to a text classification task to determine mental health problems. Inputs are transcribed speech samples from a ''structured-narrative task'' and outputs are psychiatric categories such as schizophrenia. In a preliminary trial, subjects from three groups generated speech samples: those with clinically diagnosed schizophrenia (31 patients), clinically diagnosed mania (16 patients) and controls (9 subjects). Even though the structured narrative task resulted in the use of a limited vocabulary by all subjects (only a total of 1100 different words were used), a classification performance approaching 80% accuracy was achieved for the schizophrenia versus control task. Classification performance at this level indicates that the method is suitable for diagnostic or screening purposes. It is expected that results improve further in experiments utilising free-speech samples. Diagnostic categories in psychiatry can be broad and heterogeneous, e.g. schizophrenia, which includes a range of very different symptoms. In further experiments, clustering techniques are used to extract task-relevant diagnostic categories from psychiatric reports. In these reports, psychiatrists typically include biographic, background and referral information, a description of symptoms and an opinion on treatment recommendations. At the task level, diagnostic reports are written for a specific audience or decision making body. In preliminary experiments, detailed and specific diagnostic categories have been extracted from psychiatric reports by use of unsupervised learning. These categories genuinely reflect the everyday practise of a mental health professional.