33,227 research outputs found
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
The impact of source-side syntactic reordering on hierarchical phrase-based SMT
Syntactic reordering has been demonstrated
to be helpful and effective for handling
different word orders between source
and target languages in SMT. However, in
terms of hierarchial PB-SMT (HPB), does
the syntactic reordering still has a significant
impact on its performance? This
paper introduces a reordering approach
which explores the { (DE) grammatical
structure in Chinese. We employ
the Stanford DE classifier to recognise
the DE structures in both training and
test sentences of Chinese, and then perform
word reordering to make the Chinese
sentences better match the word order
of English. The annotated and reordered
training data and test data are applied
to a re-implemented HPB system and
the impact of the DE construction is examined.
The experiments are conducted
on the NIST 2008 evaluation data and experimental
results show that the BLEU
and METEOR scores are significantly improved
by 1.83/8.91 and 1.17/2.73 absolute/
relative points respectively
Comparative Evaluation of Packet Classification Algorithms for Implementation on Resource Constrained Systems
This paper provides a comparative evaluation of a number of known classification algorithms that have been considered for both software and hardware implementation. Differently from other sources, the comparison has been carried out on implementations based on the same principles and design choices. Performance measurements are obtained by feeding the implemented classifiers with various traffic traces in the same test scenario. The comparison also takes into account implementation feasibility of the considered algorithms in resource constrained systems (e.g. embedded processors on special purpose network platforms). In particular, the comparison focuses on achieving a good compromise between performance, memory usage, flexibility and code portability to different target platforms
Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.
© 2015 Massé et al.Background: Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor. Methods: Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH). Results: The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation. Conclusion: The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier
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