403 research outputs found
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Theory-driven learning : using intra-example relationships to constrain learning
We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains
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A comparative survey of integrated learning systems
This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework
When is Enough Good Enough in Gravitational Wave Source Modeling?
A typical approach to developing an analysis algorithm for analyzing
gravitational wave data is to assume a particular waveform and use its
characteristics to formulate a detection criteria. Once a detection has been
made, the algorithm uses those same characteristics to tease out parameter
estimates from a given data set. While an obvious starting point, such an
approach is initiated by assuming a single, correct model for the waveform
regardless of the signal strength, observation length, noise, etc. This paper
introduces the method of Bayesian model selection as a way to select the most
plausible waveform model from a set of models given the data and prior
information. The discussion is done in the scientific context for the proposed
Laser Interferometer Space Antenna.Comment: 7 pages, 2 figures, proceedings paper for the Sixth International
LISA Symposiu
Consistent Estimation of Mixed Memberships with Successive Projections
This paper considers the parameter estimation problem in Mixed Membership
Stochastic Block Model (MMSB), which is a quite general instance of random
graph model allowing for overlapping community structure. We present the new
algorithm successive projection overlapping clustering (SPOC) which combines
the ideas of spectral clustering and geometric approach for separable
non-negative matrix factorization. The proposed algorithm is provably
consistent under MMSB with general conditions on the parameters of the model.
SPOC is also shown to perform well experimentally in comparison to other
algorithms
Information-based objective functions for active data selection
Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness
A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
This paper presents a novel spectral algorithm with additive clustering
designed to identify overlapping communities in networks. The algorithm is
based on geometric properties of the spectrum of the expected adjacency matrix
in a random graph model that we call stochastic blockmodel with overlap (SBMO).
An adaptive version of the algorithm, that does not require the knowledge of
the number of hidden communities, is proved to be consistent under the SBMO
when the degrees in the graph are (slightly more than) logarithmic. The
algorithm is shown to perform well on simulated data and on real-world graphs
with known overlapping communities.Comment: Journal of Theoretical Computer Science (TCS), Elsevier, A Para\^itr
Learning in Parallel
In this paper, we extend Valiant's sequential model of concept learning from
examples [Valiant 1984] and introduce models for the e cient learning of concept classes
from examples in parallel. We say that a concept class is NC-learnable if it can be learned
in polylog time with a polynomial number of processors. We show that several concept
classes which are polynomial-time learnable are NC-learnable in constant time. Some other
classes can be shown to be NC-learnable in logarithmic time, but not in constant time.
Our main result shows that other classes, such as s-fold unions of geometrical objects in
Euclidean space, which are polynomial-time learnable by a greedy set cover technique,
are NC-learnable using a non-greedy technique. We also show that (unless P RNC)
several polynomial-time learnable concept classes related to linear programming are not
NC-learnable. Equivalence of various parallel learning models and issues of fault-tolerance
are also discussed
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