5565732
Instance Pruning Techniques
The nearest neighbor algorithm and its derivatives are often quite successful at learning a concept from a training set and providing good generalization on subsequent input vectors. However, these techniques often retain the entire training set in memory, resulting in large memory requirements and slow execution speed, as well as a sensitivity to noise. This paper provides a discussion of issues related to reducing the number of instances retained in memory while maintaining (and sometimes improving) generalization accuracy, and mentions algorithms other researchers have used to address this problem. It presents three intuitive noise-tolerant algorithms that can be used to prune instances from the training set. In experiments on 29 applications, the algorithm that achieves the highest reduction in storage also results in the highest generalization accuracy of the three methods.
5584468
Improved Heterogeneous Distance Functions
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 proposes three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference Metric (IVDM), and the Windowed Value Difference Metric (WVDM). These new distance functions are designed to handle applications with nominal attributes, continuous attributes, or both. In experiments on 48 applications the new distance metrics achieve higher classification accuracy on average than three previous distance functions on those datasets that have both nominal and continuous attributes.
652801
The general inefficiency of batch training for gradient descent learning
Gradient descent training of neural networks can be done in either a batch or on-line manner. A widely held myth in the neural network community is that batch training is as fast or faster and/or more 'correct' than on-line training because it supposedly uses a better approximation of the true gradient for its weight updates. This paper explains why batch training is almost always slower than on-line training-often orders of magnitude slower-especially on large training sets. The main reason is due to the ability of on-line training to follow curves in the error surface throughout each epoch, which allows it to safely use a larger learning rate and thus converge with less iterations through the training data. Empirical results on a large (20,000-instance) speech recognition task and on 26 other learning tasks demonstrate that convergence can be reached significantly faster using on-line training than batch training, with no apparent difference in accuracy.
9824069
Data Mining Support for Case-Based Collaborative Recommendation
This paper describes ongoing research which aims to enhance collaborative recommendation techniques in the context of PTV, an applied recommender system for the TV listings domain. We have developed a case-based perspective on PTV's collaborative recommendation component, viewing the sparsity problem in collaborative filtering as one of updating and maintaining similarity knowledge for case-based systems. Our approach applies data mining techniques to extract relationships between program items that can be used to address the sparsity/ maintenance problem, as well as employing recommendation ranking that combines user similarities and item similarities to deliver more effective recommendation orderings.
56631463
Explicit vs Implicit Profiling - A Case-Study in Electronic Programme Guides
In this paper, we evaluate the use of implicit interest indicators as the basis for user profiling in the Digital TV domain. Research in more traditional domains, such as Web browsing or Usenet News, indicates that some implicit interest indicators (e.g., read-time and mouse movements) are capable of serving as alternative to explicit profile information such as user ratings. Consequently, the key question we wish to answer relates to the type of implicit indicators that can be identified within the DTV domain and the extent to which they can accurately reflect a user's true preferences.
11169287
Acquiring Case Adaptation Knowledge: A Hybrid Approach
The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance.
26356097
Adaptive Presentation and Navigation for Geospatial Imagery Tasks
Advances in technology for digital image capture have given rise to information overload problems in the geosciences and fields that rely on geospatial image retrieval. To help address such imagery information overload, our research is developing methods to extract and apply contextual knowledge relating user task goals to the images being used. As users analyze imagery retrieved to support specific tasks, multiple relevant images and salient image content can be captured together with task annotations to provide a basis for contextual knowledge management support. This paper describes how our environment for image interaction leverages captured task-based knowledge to support adaptive presentation and navigation in the space of available imagery.
6823058
DubLet: An Online CBR System for Rental Property Recommendation
Searching for accommodation in Dublin's fast-moving rental market can be a difficult and frustrating process. Ideally, the accommodation seeker would have access to a support system that could consolidate available rental information, recommend suitable properties, and always be at hand with the most current information. This paper describes the fully implemented DubLet system to provide just such support. DubLet is an application that employs core CBR methods to recommend rental properties drawn from a number of online sources using Information Extraction techniques. User interaction is provided both via web-browser and via WAP mobile phone. The paper gives an overview of the DubLet system implementation, presents an evaluation of the extraction and recommendation methods, and describes future system directions.