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Author Balasubramanian, Vineeth ♦ Panchanathan, Sethuraman ♦ Chakraborty, Shayok
Source CiteSeerX
Content type Text
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Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Transductive Confidence Machine ♦ Head Pose Classification ♦ Multiple Cue Integration ♦ Confidence Value ♦ Online Learning Scenario ♦ Confidence Measure ♦ Prediction Accuracy ♦ Multiple Hypothesis ♦ Statistical Approach ♦ Feret Database ♦ Predicted Class Label ♦ Transductive Learning ♦ Critical Pattern Recognition Application ♦ Single Test Hypothesis ♦ Important Facet ♦ Online Setting ♦ Multiple Cue ♦ Face Image ♦ Online Learning ♦ Online Learning Approach ♦ Test Data Point ♦ Heuristic Confidence Measure ♦ Training Data Increase ♦ Significant Boost
Abstract An important facet of learning in an online setting is the confidence associated with a prediction on a given test data point. In an online learning scenario, it would be expected that the system can increase its confidence of prediction as training data increases. We present a statistical approach in this work to associate a confidence value with a predicted class label in an online learning scenario. Our work is based on the existing work on Transductive Confidence Machines (TCM) [1], which provided a methodology to define a heuristic confidence measure. We applied this approach to the problem of head pose classification from face images, and extended the framework to compute a confidence value when multiple cues are extracted from images to perform classification. Our approach is based on combining the results of multiple hypotheses and obtaining an integrated p-value to validate a single test hypothesis. From our experiments on the widely accepted FERET database, we obtained results which corroborated the significance of confidence measures- particularly, in online learning approaches. We could infer from our results with transductive learning that using confidence measures in online learning could yield significant boosts in the prediction accuracy, which would be very useful in critical pattern recognition applications. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study