It is important to know the structure of the sentence for many NLP tasks. In this paper we propose a hybrid method for clause splitting in unrestricted English texts which requires less human work than existing approaches. The results of a machine learning algorithm, trained on an annotated corpus, are processed by a shallow rule-based module in order to improve the accuracy of the method. The ...

In a multiple-instance representation, each learning example is represented by a “bag” of fixed-length “feature vectors”. Such a representation, lying somewhere between propositional and first-order representation, offers a tradeoff between the two. This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. It describes NAIVE-RIPPERMI, an implementa...

We present an implementation of speculative parallelism in the realm of deductive and inductive reasoning systems. Machine learning, a form of induction, can be thought of as a search for a generalizing rule that summarizes a collection of data. In this work, we broach the search for such a rule by simultaneously traversing the search space at ten different starting points; ten ranking algorith...

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse’s assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work ...

Active learning is a rapidly growing field of machine learning which aims at reducing the labeling effort of the oracle (human expert) in acquiring informative training samples in domains where the cost of labeling is high. Associative classification is a well established prediction method which possesses the advantages of high accuracy and faster learning rates in classification. In this paper...

apriori algorithm is the most popular algorithm in association rules mining. one of the problems the apriori algorithm is that the user must specify a minimum support threshold. consider that a user wants to implement the apriori algorithm on a database with millions of transactions; users will not have the necessary knowledge about all the transactions in the database and therefore cannot dete...

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the applicati...

The Default&Refine algorithm is a new rule-based learning algorithm that was developed as an accurate and efficient pronunciation prediction mechanism for speech processing systems. The algorithm exhibits a number of attractive properties including rapid generalisation from small training sets, good asymptotic accuracy, robustness to noise in the training data, and the production of compact rul...

We present an algorithm (BBG) for inductive learning from examples that outputs a rule list. BBG uses a combination of greedy and branch-and-bound techniques, and naturally handles noisy or stochastic learning situations. We also present the results of an empirical study comparing BBG with Quinlan's C4.5 on 1050 synthetic data sets. We nd that BBG greatly outperforms C4.5 on rule-oriented probl...