107,919 research outputs found
Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction
Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659
The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning
Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Test Set Diameter: Quantifying the Diversity of Sets of Test Cases
A common and natural intuition among software testers is that test cases need
to differ if a software system is to be tested properly and its quality
ensured. Consequently, much research has gone into formulating distance
measures for how test cases, their inputs and/or their outputs differ. However,
common to these proposals is that they are data type specific and/or calculate
the diversity only between pairs of test inputs, traces or outputs.
We propose a new metric to measure the diversity of sets of tests: the test
set diameter (TSDm). It extends our earlier, pairwise test diversity metrics
based on recent advances in information theory regarding the calculation of the
normalized compression distance (NCD) for multisets. An advantage is that TSDm
can be applied regardless of data type and on any test-related information, not
only the test inputs. A downside is the increased computational time compared
to competing approaches.
Our experiments on four different systems show that the test set diameter can
help select test sets with higher structural and fault coverage than random
selection even when only applied to test inputs. This can enable early test
design and selection, prior to even having a software system to test, and
complement other types of test automation and analysis. We argue that this
quantification of test set diversity creates a number of opportunities to
better understand software quality and provides practical ways to increase it.Comment: In submissio
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