19,899 research outputs found
Discrete MDL Predicts in Total Variation
The Minimum Description Length (MDL) principle selects the model that has the
shortest code for data plus model. We show that for a countable class of
models, MDL predictions are close to the true distribution in a strong sense.
The result is completely general. No independence, ergodicity, stationarity,
identifiability, or other assumption on the model class need to be made. More
formally, we show that for any countable class of models, the distributions
selected by MDL (or MAP) asymptotically predict (merge with) the true measure
in the class in total variation distance. Implications for non-i.i.d. domains
like time-series forecasting, discriminative learning, and reinforcement
learning are discussed.Comment: 15 LaTeX page
Generalized Kernel-based Visual Tracking
In this work we generalize the plain MS trackers and attempt to overcome
standard mean shift trackers' two limitations.
It is well known that modeling and maintaining a representation of a target
object is an important component of a successful visual tracker.
However, little work has been done on building a robust template model for
kernel-based MS tracking. In contrast to building a template from a single
frame, we train a robust object representation model from a large amount of
data. Tracking is viewed as a binary classification problem, and a
discriminative classification rule is learned to distinguish between the object
and background. We adopt a support vector machine (SVM) for training. The
tracker is then implemented by maximizing the classification score. An
iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page
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