Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-based methods use experiential data more efficiently than modelfree approaches but often require exhaustive exploration to learn an accurate model of the domain. We present an algorithm, Reinforcement Learning with Decis...

This paper presents a new methodology, based on the classical decision trees, to get a suitable set of context dependent sublexical units for Basque Continuous Speech Recognition (CSR). The original method proposed by Bahl [1] was applied as the benchmark. Then two new features were added: a data massaging to emphasise the data and a fast and efficient Growing and Pruning algorithm for DT const...

The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). This paper presents two hybrid approaches for modeling IDS. Decision trees (DT) and support vector machines (SVM) are combined as a hierarchical hybrid intelligent system model (DT–SVM) and an ensemble approach combining the base ...

Decision tree (DT) is one of the most popular classification algorithms in data mining and machine learning. However, the performance of DT based credit scoring model is often relatively poorer than other techniques. This is mainly due to two reasons: DT is easily affected by (1) the noise data and (2) the redundant attributes of data under the circumstance of credit scoring. In this study, we ...

Although artificial neural networks can represent a variety of complex systems with a high degree of accuracy, these connectionist models are difficult to interpret. This significantly limits the applicability of neural networks in practice, especially where a premium is placed on the comprehensibility or reliability of systems. A novel artificial neural-network decision tree algorithm (ANN-DT)...

In this article, we compare decision trees (DT) and support vector machines (SVM) in classifying gene expressions. With the explosion of genome research, tremendous amount of data have been made available and a deep insight study becomes demanding. Among various kinds of gene analysis approaches being developed, sequence based gene expression classification shows the importance due to its abili...

Prosody structure prediction plays an important role in text-tospeech (TTS) conversion systems, where it is a prior step to parametric prosody prediction. Dynamic programming (DP) and decision tree based methods (DT) are widely used for this purpose, but both have well-known limitations. In this paper, we present a combination of both methods, explore the relationship between corpus size and ac...

Popular decision tree (DT) algorithms such as ID3, C4.5, CART, CHAID and QUEST may have different results using same data set. They consist of components which have similar functionalities. These components implemented on different ways and they have different performance. The best way to get an optimal DT for a data set is one that use component-based design, which enables user to intelligentl...

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to b...

Machine learning approaches based on decision trees (DTs) have been proposed for classifying networking traffic. Although this technique has been proven to have the ability to classify encrypted and unknown traffic, the software implementation of DT cannot cope with the current speed of packet traffic. In this paper, hardware architecture of decision tree is proposed on NetFPGA platform. The pr...