1,720,966 research outputs found
An experimental comparison of Random Projection ensembles with linear kernel SVMs and Bagging and BagBoosting methods for the classification of gene expression data
No full textIn this work we experimentally analyze ensemble algorithms based on Random Subspace and Random Plus-Minus-One Projection, comparing them to the results obtained in literature by the application of Bagging and BagBoosting on the same data sets used in our experiments: Colon and Leukemia. In this work we concentrate on the application of random projection (Badoiu et al., 2006) ensemble of SVMs, with the aim to improve the accuracy of classification, both through SVMs that represent the state-of-the-art in gene expression data analysis (Vapnik, 1998) (Pomeroy et al., 2002), and through the ensemble methods, used in our work to enhance the classification accuracy and capability. Ensemble methods, in fact, train multiple classifiers and combine them to reduce the generalization error of the multi-classifiers system. To make possible the comparison of our results with those obtained in literature by the application of Bagging and BagBoosting, in this works we concentrate on SVMs with linear kernel
Clever Pac-man
No full textIn this paper we show how combining fuzzy sets and reinforcement learning a winning agent can be created for the popular Pac-man game. Key elements are the classification of the state into a few fuzzy classes that makes the problem manageable. Pac-man policy is defined in terms of fuzzy actions that are
defuzzified to produce the actual Pac-man move. A few heuristics allow making the Pac-man strategy very similar to the Human one. Ghosts agents, on their side, are endowed also with fuzzy behavior inspired by original design strategy.
Performance of this Pac-man is shown to be superior to those of other AI-based Pac-man described in the literature
A novel approach for geometric clustering based on tensor voting framework
No full textIn this work we propose a novel geometric clustering algorithm based on the Tensor Voting Framework (TVF). More precisely, we propose the construction of a weighted graph by means of the information diffused by TVF during the vote casting step. This graph, which summarizes informations related to the manifold geometric structure, was used for clustering purposes. To this aim, we applied the well known Dijkstra and Ford Fulkerson algorithms to recursively separate weakly connected graph components. We performed preliminary tests, comparing our algorithm with that obtained by employing a weighted version of the -NN graph. The obtained results on both synthetic and real data show that the proposed technique is promising. To test our algorithm on real datasets, we preprocessed graylevel input images by extracting their edge pixel points
Creating long gait animation sequences through Reinforcement Learning
No full textIn this paper we present how, using a careful definition of a state function, long animation sequences can be created joining clips from a database.
Each next clip is chosen in real-time by a controller optimizing a cost function on the state function; this allows the user interact in real-time with the digital character. We analyze here two possible cost functions, one that is based on the evaluation of the compatibility of the next clip and one based on reinforcement learning in which the global policy of the controller is evaluated
Identification of promoter regions in genomic sequences by 1-dimensional constraint clustering
No full textSize constrained clustering has been recently proposed to embed "a priori"
knowledge in clustering methods. By exploiting the "string property" we propose an exact and efficient algorithm based on dynamic programming techniques to solve size-constrained one-dimensional clustering problems. We show the applicability of the proposed method in a difficult computational biology problem: the prediction of the transcription start sites of genes. The obtained experimental results
clearly show the potential of the proposed approach when compared with previously published methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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