Showing papers in "Advances in Artificial Intelligence in 2009"

A survey of collaborative filtering techniques

Abstract: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

A general rate K/N convolutional decoder based on neural networks with stopping criterion

Abstract: A novel algorithm for decoding a general rate K/Nconvolutional code based on recurrent neural network (RNN) is described and analysed The algorithm is introduced by outlining the mathematical models of the encoder and decoder A number of strategies for optimising the iterative decoding process are proposed, and a simulator was also designed in order to compare the Bit Error Rate (BER) performance of the RNN decoder with the conventional decoder that is based on Viterbi Algorithm (VA) The simulation results show that this novel algorithm can achieve the same bit error rate and has a lower decoding complexity Most importantly this algorithm allows parallel signal processing, which increases the decoding speed and accommodates higher data rate transmission These characteristics are inherited from a neural network structure of the decoder and the iterative nature of the algorithm, that outperform the conventional VA algorithm

Bayesian unsupervised learning of DNA regulatory binding regions

Abstract: Identification of regulatory binding motifs, that is, short specific words, within DNA sequences is a commonly occurring problem in computational bioinformatics. A wide variety of probabilistic approaches have been proposed in the literature to either scan for previously known motif types or to attempt de novo identification of a fixed number (typically one) of putative motifs. Most approaches assume the existence of reliable biodatabase information to build probabilistic a priori description of the motif classes. Examples of attempts to do probabilistic unsupervised learning about the number of putative de novo motif types and their positions within a set of DNA sequences are very rare in the literature. Here we show how such a learning problem can be formulated using a Bayesian model that targets to simultaneously maximize the marginal likelihood of sequence data arising under multiple motif types as well as under the background DNA model, which equals a variable length Markov chain. It is demonstrated how the adopted Bayesian modelling strategy combined with recently introduced nonstandard stochastic computation tools yields a more tractable learning procedure than is possible with the standard Monte Carlo approaches. Improvements and extensions of the proposed approach are also discussed.

A new information measure based on example-dependent misclassification costs and its application in decision tree learning

Abstract: This article describes how the costs of misclassification given with the individual training objects for classification learning can be used in the construction of decision trees for minimal cost instead of minimal error class decisions. This is demonstrated by defining modified, cost-dependent probabilities, a new, cost-dependent information measure, and using a cost-sensitive extension of the CAL5 algorithm for learning decision trees. The cost-dependent information measure ensures the selection of the (local) next best, that is, cost-minimizing, discriminating attribute in the sequential construction of the classification trees. This is shown to be a cost-dependent generalization of the classical information measure introduced by Shannon, which only depends on classical probabilities. It is therefore of general importance and extends classic information theory, knowledge processing, and cognitive science, since subjective evaluations of decision alternatives can be included in entropy and the transferred information. Decision trees can then be viewed as cost-minimizing decoders for class symbols emitted by a source and coded by feature vectors. Experiments with two artificial datasets and one application example show that this approach is more accurate than a method which uses class dependent costs given by experts a priori.

Analysis of enhanced access selection methods and end-user perception in multi-operator environments

Abstract: Nowadays, the access selection methods are done based on operator incentives using static and predefined rules that are not sufficient once the number of deployed access technologies and mobile ser ...