4,459 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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Towards robust real-world historical handwriting recognition
In this thesis, we make a bridge from the past to the future by using artificial-intelligence methods for text recognition in a historical Dutch collection of the Natuurkundige Commissie that explored Indonesia (1820-1850). In spite of the successes of systems like 'ChatGPT', reading historical handwriting is still quite challenging for AI. Whereas GPT-like methods work on digital texts, historical manuscripts are only available as an extremely diverse collections of (pixel) images. Despite the great results, current DL methods are very data greedy, time consuming, heavily dependent on the human expert from the humanities for labeling and require machine-learning experts for designing the models. Ideally, the use of deep learning methods should require minimal human effort, have an algorithm observe the evolution of the training process, and avoid inefficient use of the already sparse amount of labeled data. We present several approaches towards dealing with these problems, aiming to improve the robustness of current methods and to improve the autonomy in training. We applied our novel word and line text recognition approaches on nine data sets differing in time period, language, and difficulty: three locally collected historical Latin-based data sets from Naturalis, Leiden; four public Latin-based benchmark data sets for comparability with other approaches; and two Arabic data sets. Using ensemble voting of just five neural networks, a level of accuracy was achieved which required hundreds of neural networks in earlier studies. Moreover, we increased the speed of evaluation of each training epoch without the need of labeled data
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