Training Examples (training + example)

Distribution by Scientific Domains


Selected Abstracts


THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

COMPUTATIONAL INTELLIGENCE, Issue 2 2006
Moshe Koppel
Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples. [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]


Incrementally updating a hybrid rule base based on empirical data

EXPERT SYSTEMS, Issue 4 2007
Jim Prentzas
Abstract: Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. One way that the neurules can be produced is from training examples/patterns, extracted from empirical data. However, in certain application fields not all of the training examples are available a priori. A number of them become available over time. In those cases, updating the neurule base is necessary. In this paper, methods for updating a hybrid rule base, consisting of neurules, to reflect the availability of new training examples are presented. They can be considered as a type of incremental learning method that retains the entire induced hypothesis and all past training examples. The methods are efficient, since they require the least possible retraining effort and the number of neurules produced is kept as small as possible. Experimental results that prove the above argument are presented. [source]


A hybrid Bayesian back-propagation neural network approach to multivariate modelling

INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 8 2003
C. G. Chua
Abstract There is growing interest in the use of back-propagation neural networks to model non-linear multivariate problems in geotehnical engineering. To overcome the shortcomings of the conventional back-propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back-propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back-propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright 2003 John Wiley & Sons, Ltd. [source]


ON MULTI-CLASS COST-SENSITIVE LEARNING

COMPUTATIONAL INTELLIGENCE, Issue 3 2010
Zhi-Hua Zhou
Rescaling,is possibly the most popular approach to cost-sensitive learning. This approach works by rebalancing the classes according to their costs, and it can be realized in different ways, for example, re-weighting or resampling the training examples in proportion to their costs, moving the decision boundaries of classifiers faraway from high-cost classes in proportion to costs, etc. This approach is very effective in dealing with two-class problems, yet some studies showed that it is often not so helpful on multi-class problems. In this article, we try to explore why the rescaling approach is often helpless on multi-class problems. Our analysis discloses that the rescaling approach works well when the costs are,consistent, while directly applying it to multi-class problems with,inconsistent,costs may not be a good choice. Based on this recognition, we advocate that before applying the rescaling approach, the,consistency,of the costs must be examined at first. If the costs are consistent, the rescaling approach can be conducted directly; otherwise it is better to apply rescaling after decomposing the multi-class problem into a series of two-class problems. An empirical study involving 20 multi-class data sets and seven types of cost-sensitive learners validates our proposal. Moreover, we show that the proposal is also helpful for class-imbalance learning. [source]