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Learning Framework (learning + framework)

Distribution by Scientific Domains
Information Science and Computing37%

Selected Abstracts

HIGH-DIMENSIONAL LEARNING FRAMEWORK FOR ADAPTIVE DOCUMENT FILTERING,

COMPUTATIONAL INTELLIGENCE, Issue 1 2003
Wai Lam
We investigate the unique requirements of the adaptive textual document filtering problem and propose a new high-dimensional on-line learning framework, known as the REPGER (relevant feature pool with good training example retrieval rule) algorithm to tackle this problem. Our algorithm possesses three characteristics. First, it maintains a pool of selective features with potentially high predictive power to predict document relevance. Second, besides retrieving documents according to their predicted relevance, it also retrieves incoming documents that are considered good training examples. Third, it can dynamically adjust the dissemination threshold throughout the filtering process so as to maintain a good filtering performance in a fully interactive environment. We have conducted experiments on three document corpora, namely, Associated Press, Foreign Broadcast Information Service, and Wall Street Journal to compare the performance of our REPGER algorithm with two existing on-line learning algorithms. The results demonstrate that our REPGER algorithm gives better performance most of the time. Comparison with the TREC (Text Retrieval Conference) adaptive text filtering track participants was also made. The result shows that our REPGER algorithm is comparable to them. [source]

AN ADAPTIVE LEARNING FRAMEWORK FOR FORECASTING SEASONAL WATER ALLOCATIONS IN IRRIGATED CATCHMENTS

NATURAL RESOURCE MODELING, Issue 3 2010
SHAHBAZ KHAN
Abstract This paper describes an adaptive learning framework for forecasting end-season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end-irrigation-season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end-season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk-management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST-SOI incorporated) demonstrated ANN capability of forecasting end-of-season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model. [source]

HIGH-DIMENSIONAL LEARNING FRAMEWORK FOR ADAPTIVE DOCUMENT FILTERING,

COMPUTATIONAL INTELLIGENCE, Issue 1 2003
Wai Lam
We investigate the unique requirements of the adaptive textual document filtering problem and propose a new high-dimensional on-line learning framework, known as the REPGER (relevant feature pool with good training example retrieval rule) algorithm to tackle this problem. Our algorithm possesses three characteristics. First, it maintains a pool of selective features with potentially high predictive power to predict document relevance. Second, besides retrieving documents according to their predicted relevance, it also retrieves incoming documents that are considered good training examples. Third, it can dynamically adjust the dissemination threshold throughout the filtering process so as to maintain a good filtering performance in a fully interactive environment. We have conducted experiments on three document corpora, namely, Associated Press, Foreign Broadcast Information Service, and Wall Street Journal to compare the performance of our REPGER algorithm with two existing on-line learning algorithms. The results demonstrate that our REPGER algorithm gives better performance most of the time. Comparison with the TREC (Text Retrieval Conference) adaptive text filtering track participants was also made. The result shows that our REPGER algorithm is comparable to them. [source]

Active learning for constructing transliteration lexicons from the Web

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 1 2008
Jin-Shea Kuo
This article presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine transliteration and acquires knowledge iteratively from the Web. We study the unsupervised learning and the active learning strategies that minimize human supervision in terms of data labeling. The learning process refines the PSM and constructs a transliteration lexicon at the same time. We evaluate the proposed PSM and its learning algorithm through a series of systematic experiments, which show that the proposed framework is reliably effective on two independent databases. [source]

AN ADAPTIVE LEARNING FRAMEWORK FOR FORECASTING SEASONAL WATER ALLOCATIONS IN IRRIGATED CATCHMENTS

NATURAL RESOURCE MODELING, Issue 3 2010
SHAHBAZ KHAN
Abstract This paper describes an adaptive learning framework for forecasting end-season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end-irrigation-season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end-season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk-management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST-SOI incorporated) demonstrated ANN capability of forecasting end-of-season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model. [source]

Paying the piper: Foundation evaluation capacity calls the tune

NEW DIRECTIONS FOR EVALUATION, Issue 119 2008
Teresa R. Behrens
An overview is presented of forces that have shaped how public and private funders approach evaluation, including the challenges that funders, and particularly foundations, face in effectively using evaluation within an organizational learning framework. Even with internal organizational challenges to learning, foundations are increasingly embracing knowledge management and performance measurement as tools for reflection, learning, and accountability. The authors argue that funders must ultimately adopt a more systems-oriented view of their work if they are to effectively use and learn from evaluation, and to adopt both funding strategies and evaluation approaches that are situated in a systems change paradigm. Emerging tools to help foundations incorporate systems approaches in grantmaking are highlighted. © Wiley Periodicals, Inc. [source]

A reflective learning framework to evaluate CME effects on practice reflection

THE JOURNAL OF CONTINUING EDUCATION IN THE HEALTH PROFESSIONS, Issue 2 2010
Kit H. Leung PhD Researcher
Abstract Introduction: The importance of reflective practice is recognized by the adoption of a reflective learning model in continuing medical education (CME), but little is known about how to evaluate reflective learning in CME. Reflective learning seldom is defined in terms of specific cognitive processes or observable performances. Competency-based evaluation rarely is used for evaluating CME effects. To bridge this gap, reflective learning was defined operationally in a reflective learning framework (RLF). The operationalization supports observations, documentation, and evaluation of reflective learning performances in CME, and in clinical practice. In this study, the RLF was refined and validated as physician performance was evaluated in a CME e-learning activity. Methods: Qualitative multiple-case study wherein 473 practicing family physicians commented on research-based synopses after reading and rating them as an on-line CME learning activity. These comments formed 2029 cases from which cognitive tasks were extracted as defined by the RLF with the use of a thematic analysis. Frequencies of cognitive tasks were compared in a cross-case analysis. Results: Four RLF cognitive processes and 12 tasks were supported. Reflective learning was defined as 4 interrelated cognitive processes: Interpretation, Validation, Generalization, and Change, which were specified by 3 observable cognitive tasks, respectively. These 12 tasks and related characteristics were described in an RLF codebook for future use. Discussion: Reflective learning performances of family physicians were evaluated. The RLF and its codebook can be used for integrating reflective learning into CME curricula and for developing competency-based assessment. Future research on potential uses of the RLF should involve participation of CME stakeholders. [source]