In instance-based classifiers, there is a need for storing a large number of samples as training set. In this work, we propose an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC). IRAHC removes non-border (interior) instances and keeps border and near border ones. This paper presents an instance reduction p...

Decision heuristics are often described as fast and frugal, taking little time and few computations to make a good decision (Gigerenzer & Todd, 1999). Fast & Frugal Trees (F&FTs) are a type of decision heuristic that are a special case of decision trees in which there is a possible exit out of the decision process at every node in the tree (Luan, Schooler, & Gigerenzer, 2011). We present predic...

In this paper, we present a systematic evaluation of a hybrid approach of combined rule-based filtering and machine learning to Dutch coreference resolution. Through the application of a selection of linguistically-motivated negative and positive filters, which we apply in isolation and combined, we study the effect of these filters on precision and recall using two different learning technique...

Local SVM is a classification method that combines instance-based learning and statistical machine learning. It builds an SVM on the feature space neighborhood of the query point in the training set and uses it to predict its class. There is both empirical and theoretical evidence that Local SVM can improve over SVM and kNN in terms of classification accuracy, but the computational cost of the ...

This chapter is concerned with the enhancement of planning systems using techniques from Machine Learning in order to automatically configure their planning parameters according to the morphology of the problem in hand. It presents two different adaptive systems that set the planning parameters of a highly adjustable planner based on measurable characteristics of the problem instance. The plann...

Point patterns are sets or multi-sets of unordered points that arise in numerous data analysis problems. This article proposes a framework for model-based point pattern learning using point process theory. Likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty dete...

This paper proposes a hybrid of handcrafted rules and a machine learning method for chunking Korean. In the partially free word-order languages such as Korean and Japanese, a small number of rules dominate the performance due to their well-developed postpositions and endings. Thus, the proposed method is primarily based on the rules, and then the residual errors are corrected by adopting a memo...

The compound nouns are freely composed in Korean, since it is possible to concatenate independent nouns without a postposition. Therefore, the systems that handle compound nouns such as machine translation and information retrieval have to decompose them into single nouns for the further correct analysis of texts. This paper proposes the GECORAM (GEneralized COmbination of Rule-based learning A...

We apply rule induction, classifier combination and meta-learning (stacked classifiers) to the problem of bootstrapping high accuracy automatic annotation of corpora with pronunciation information. The task we address in this paper consists of generating phonemic representations reflecting the Flemish and Dutch pronunciations of a word on the basis of its orthographic representation (which in t...

Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Alg...