Journal of Artificial Intelligence Research
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Unsupervised Methods for Determining Object and Relation Synonyms on the Web
Journal of Artificial Intelligence Research - Tập 34 - Trang 255-296
The task of identifying synonymous relations and objects, or synonym resolution,
is critical for high-quality information extraction. This paper investigates
synonym resolution in the context of unsupervised information extraction, where
neither hand-tagged training examples nor domain knowledge is available. The
paper presents a scalable, fully-implemented system that runs in O(KN log N)
time in ...
Active Learning with Statistical Models
Journal of Artificial Intelligence Research - Tập 4 - Trang 129-145
For many types of machine learning algorithms, one can compute the statistically
`optimal' way to select training data. In this paper, we review how optimal data
selection techniques have been used with feedforward neural networks. We then
show how the same principles may be used to select data for two alternative,
statistically-based learning architectures: mixtures of Gaussians and locally
weigh...
Improved Heterogeneous Distance Functions
Journal of Artificial Intelligence Research - Tập 6 - Trang 1-34
Instance-based learning techniques typically handle continuous and linear input
values well, but often do not handle nominal input attributes appropriately. The
Value Difference Metric (VDM) was designed to find reasonable distance values
between nominal attribute values, but it largely ignores continuous attributes,
requiring discretization to map continuous values into nominal values. This
paper...
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
Journal of Artificial Intelligence Research - Tập 61 - Trang 863-905
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered "de facto" standard in the framework of learning from imbalanced
data. This is due to its simplicity in the design of the procedure, as well as
its robustness when applied to different type of problems. Since its publication
in 2002, SMOTE has proven successful in a variety of applications from several
diff...
Evolutionary Algorithms for Reinforcement Learning
Journal of Artificial Intelligence Research - Tập 11 - Trang 241-276
There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evol...
Popular Ensemble Methods: An Empirical Study
Journal of Artificial Intelligence Research - Tập 11 - Trang 169-198
An ensemble consists of a set of individually trained classifiers (such as
neural networks or decision trees) whose predictions are combined when
classifying novel instances. Previous research has shown that an ensemble is
often more accurate than any of the single classifiers in the ensemble. Bagging
(Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two
relatively new but ...
AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks
Journal of Artificial Intelligence Research - Tập 13 - Trang 155-188
Stochastic sampling algorithms, while an attractive alternative to exact
algorithms in very large Bayesian network models, have been observed to perform
poorly in evidential reasoning with extremely unlikely evidence. To address this
problem, we propose an adaptive importance sampling algorithm, AIS-BN, that
shows promising convergence rates even under extreme conditions and seems to
outperform th...
Placement of Loading Stations for Electric Vehicles: No Detours Necessary!
Journal of Artificial Intelligence Research - Tập 53 - Trang 633-658
Compared to conventional cars, electric vehicles (EVs) still suffer from
considerably shorter cruising ranges. Combined with the sparsity of battery
loading stations, the complete transition to E-mobility still seems a long way
to go. In this paper, we consider the problem of placing as few loading stations
as possible so that on any shortest path there are sufficiently many not to run
out of ener...
Theta*: Any-Angle Path Planning on Grids
Journal of Artificial Intelligence Research - Tập 39 - Trang 533-579
Grids with blocked and unblocked cells are often used to represent terrain in
robotics and video games. However, paths formed by grid edges can be longer than
true shortest paths in the terrain since their headings are artificially
constrained. We present two new correct and complete any-angle path-planning
algorithms that avoid this shortcoming. Basic Theta* and Angle-Propagation
Theta* are both ...
Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text
Journal of Artificial Intelligence Research - Tập 30 - Trang 457-500
It is well known that utterances convey a great deal of information about the
speaker in addition to their semantic content. One such type of information
consists of cues to the speaker's personality traits, the most fundamental
dimension of variation between humans. Recent work explores the automatic
detection of other types of pragmatic variation in text and conversation, such
as emotion, decept...
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