285,129 research outputs found
Applying MDL to Learning Best Model Granularity
The Minimum Description Length (MDL) principle is solidly based on a provably
ideal method of inference using Kolmogorov complexity. We test how the theory
behaves in practice on a general problem in model selection: that of learning
the best model granularity. The performance of a model depends critically on
the granularity, for example the choice of precision of the parameters. Too
high precision generally involves modeling of accidental noise and too low
precision may lead to confusion of models that should be distinguished. This
precision is often determined ad hoc. In MDL the best model is the one that
most compresses a two-part code of the data set: this embodies ``Occam's
Razor.'' In two quite different experimental settings the theoretical value
determined using MDL coincides with the best value found experimentally. In the
first experiment the task is to recognize isolated handwritten characters in
one subject's handwriting, irrespective of size and orientation. Based on a new
modification of elastic matching, using multiple prototypes per character, the
optimal prediction rate is predicted for the learned parameter (length of
sampling interval) considered most likely by MDL, which is shown to coincide
with the best value found experimentally. In the second experiment the task is
to model a robot arm with two degrees of freedom using a three layer
feed-forward neural network where we need to determine the number of nodes in
the hidden layer giving best modeling performance. The optimal model (the one
that extrapolizes best on unseen examples) is predicted for the number of nodes
in the hidden layer considered most likely by MDL, which again is found to
coincide with the best value found experimentally.Comment: LaTeX, 32 pages, 5 figures. Artificial Intelligence journal, To
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Clustering by compression
We present a new method for clustering based on compression. The method
doesn't use subject-specific features or background knowledge, and works as
follows: First, we determine a universal similarity distance, the normalized
compression distance or NCD, computed from the lengths of compressed data files
(singly and in pairwise concatenation). Second, we apply a hierarchical
clustering method. The NCD is universal in that it is not restricted to a
specific application area, and works across application area boundaries. A
theoretical precursor, the normalized information distance, co-developed by one
of the authors, is provably optimal but uses the non-computable notion of
Kolmogorov complexity. We propose precise notions of similarity metric, normal
compressor, and show that the NCD based on a normal compressor is a similarity
metric that approximates universality. To extract a hierarchy of clusters from
the distance matrix, we determine a dendrogram (binary tree) by a new quartet
method and a fast heuristic to implement it. The method is implemented and
available as public software, and is robust under choice of different
compressors. To substantiate our claims of universality and robustness, we
report evidence of successful application in areas as diverse as genomics,
virology, languages, literature, music, handwritten digits, astronomy, and
combinations of objects from completely different domains, using statistical,
dictionary, and block sorting compressors. In genomics we presented new
evidence for major questions in Mammalian evolution, based on
whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta
hypothesis against the Theria hypothesis.Comment: LaTeX, 27 pages, 20 figure
SymbolDesign: A User-centered Method to Design Pen-based Interfaces and Extend the Functionality of Pointer Input Devices
A method called "SymbolDesign" is proposed that can be used to design user-centered interfaces for pen-based input devices. It can also extend the functionality of pointer input devices such as the traditional computer mouse or the Camera Mouse, a camera-based computer interface. Users can create their own interfaces by choosing single-stroke movement patterns that are convenient to draw with the selected input device and by mapping them to a desired set of commands. A pattern could be the trace of a moving finger detected with the Camera Mouse or a symbol drawn with an optical pen. The core of the SymbolDesign system is a dynamically created classifier, in the current implementation an artificial neural network. The architecture of the neural network automatically adjusts according to the complexity of the classification task. In experiments, subjects used the SymbolDesign method to design and test the interfaces they created, for example, to browse the web. The experiments demonstrated good recognition accuracy and responsiveness of the user interfaces. The method provided an easily-designed and easily-used computer input mechanism for people without physical limitations, and, with some modifications, has the potential to become a computer access tool for people with severe paralysis.National Science Foundation (IIS-0093367, IIS-0308213, IIS-0329009, EIA-0202067
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