1,721,148 research outputs found
MEMEX_KG: Knowledge Graphs about the cities of Lisbon, Barcelona and Paris
No full textKnowledge Graphs about the cities of Lisbon, Barcelona and Paris. The datasets are used in the following paper:
Mohamed, Hebatallah A., Sebastiano Vascon, Feliks Hibraj, Stuart James, Diego Pilutti, Alessio Del Bue, and Marcello Pelillo. "Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding." arXiv preprint arXiv:2209.03638 (2022)
A Game-Theoretic Approach to Word Sense Disambiguation
Full text linkThis article presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes. The words simultaneously update their class membership preferences according to the senses that neighboring words are likely to choose. We use distributional information to weigh the influence that each word has on the decisions of the others and semantic similarity information to measure the strength of compatibility among the choices. With this information we can formulate the word sense disambiguation problem as a constraint satisfaction problem and solve it using tools derived from game theory, maintaining the textual coherence. The model is based on two ideas: Similar words should be assigned to similar classes and the meaning of a word does not depend on all the words in a text but just on some of them. The article provides an in-depth motivation of the idea of modeling the word sense disambiguation problem in terms of game theory, which is illustrated by an example. The conclusion presents an extensive analysis on the combination of similarity measures to use in the framework and a comparison with state-of-the-art systems. The results show that our model outperforms state-of-the-art algorithms and can be applied to different tasks and in different scenarios
Using Dominant Sets for k-NN Prototype Selection
No full textk-Nearest Neighbors is surely one of the most important and widely adopted non-parametric classification methods in pattern recognition. It has evolved in several aspects in the last 50 years, and one of the most known variants consists in the usage of prototypes: a prototype distills a group of similar training points, diminishing drastically the number of comparisons needed for the classification; actually, prototypes are employed in the case the cardinality of the training data is high. In this paper, by using the dominant set clustering framework, we propose four novel strategies for the prototype generation, allowing to produce representative prototypes that mirror the underlying class structure in an expressive and effective way. Our strategy boosts the k-NN classification performance; considering heterogeneous metrics and analyzing 15 diverse datasets, we are among the best 6 prototype-based k-NN approaches, with a computational cost which is strongly inferior to all the competitors. In addition, we show that our proposal beats linear SVM in the case of a pedestrian detection scenario
A game-theoretic approach to hypergraph clustering
No full textHypergraph clustering refers to the process of extracting maximally coherent groups from a set of objects using high-order (rather than pairwise) similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into a predetermined number of classes, thereby obtaining the clusters as a by-product of the partitioning process. In this paper, we offer a radically different view of the problem. In contrast to the classical approach, we attempt to provide a meaningful formalization of the very notion of a cluster and we show that game theory offers an attractive and unexplored perspective that serves our purpose well. To this end, we formulate the hypergraph clustering problem in terms of a noncooperative multiplayer "clustering game," and show that a natural notion of a cluster turns out to be equivalent to a classical (evolutionary) game-theoretic equilibrium concept. We prove that the problem of finding the equilibria of our clustering game is equivalent to locally optimizing a polynomial function over the standard simplex, and we provide a discrete-time high-order replicator dynamics to perform this optimization, based on the Baum-Eagon inequality. Experiments over synthetic as well as real-world data are presented which show the superiority of our approach over the state of the art. © 2013 IEEE
On the Combination of Information-Theoretic Kernels with Generative Embeddings
No full textlassical methods to obtain classifiers for structured objects (e.g., sequences, images) are based on generative models and adopt a classical generative Bayesian framework. To embrace discriminative approaches (namely, support vector machines), the objects have to be mapped/embedded onto a Hilbert space; one way that has been proposed to carry out such an embedding is via generative models (maybe learned from data). This type of hybrid discriminative/generative approach has been recently shown to outperform classifiers obtained directly from the generative model upon which the embedding is built.Discriminative approaches based on generative embeddings involve two key components: a generative model used to define the embedding; a discriminative learning algorithms to obtain a (maybe kernel) classifier. The literature on generative embedding is essentially focused on defining the embedding, and some standard off-the-shelf kernel and learning algorithm are usually adopted. Recently, we have proposed a different approach that exploits the probabilistic nature of generative embeddings, by using information-theoretic kernels defined on probability distributions. In this chapter, we review this approach and its building blocks. We illustrate the performance of this approach on two medical applications
L'uomo e le macchine intelligenti. Come l'intelligenza artificiale sta cambiando le nostre vite
No full textSimone Arnaldi, docente di Sociologia generale e Sociologia politica dell'Università di Trieste; Marcello Pelillo, docente di Informatica dell'Università Ca' Foscari di Venezia. Modera Simone Regina, giornalista. A cura dell'Università di Trieste in collaborazione con SISSA e ICT
Correlation Clustering with Stochastic Labellings
No full textCorrelation clustering is the problem of finding a crisp partition of the vertices of a correlation graph in such a way as to minimize the disagreements in the cluster assignments. In this paper, we discuss a relaxation to the original problem setting which allows probabilistic assignments of vertices to labels. By so doing, overlapping clusters can be captured. We also show that a known optimization heuristic can be applied to the problem formulation, but with the automatic selection of the number of classes. Additionally, we propose a simple way of building an ensemble of agreement functions sampled from a reproducing kernel Hilbert space, which allows to apply correlation clustering without the empirical estimation of pairwise correlation values
Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets
No full text20th International Conference on Image Analysis and Processing (ICIAP) -- SEP 09-13, 2019 -- Univ Trento, Fac Law, Trento, ITALYAslan, Sinem/0000-0003-0068-6551The availability of large-scale data sets is an essential prerequisite for deep learning based semantic segmentation schemes. Since obtaining pixel-level labels is extremely expensive, supervising deep semantic segmentation networks using low-cost weak annotations has been an attractive research problem in recent years. in this work, we explore the potential of Constrained Dominant Sets (CDS) for generating multi-labeled full mask predictions to train a fully convolutional network (FCN) for semantic segmentation. Our experimental results show that using CDS's yields higher-quality mask predictions compared to methods that have been adopted in the literature for the same purpose.Int Assoc Pattern Recognit, Italian Assoc Comp Vis, Pattern Recognit & Machine Learning, Univ Trento, Fondazione Bruno KesslerScientific and Technological Research Council of Turkey under TUBITAK [BIDEB-2219, 1059B191701102]This work is supported by the Scientific and Technological Research Council of Turkey under TUBITAK BIDEB-2219 grant no 1059B191701102
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