Competition policies in emerging economies: lessons and challenges from Central America and Mexico

Incluye Bibliografía

Models of privatization and development of competition in telecommunications in Central America and Mexico

Incluye Bibliografía

Competition and regulation in the banking system of Central America and Mexico: a comparative study

Incluye Bibliografía

Advantages and limitations of Costa Rica's experience in competition policy. A benchmark for the rest of the countries in the Central American region

Incluye Bibliografía

Banking competition in Mexico

Incluye Bibliografía

Cooperation on competition policy in Latin American and Caribbean bilateral trade agreements

Incluye Bibliografía

Legal and economic interfaces between antidumping and competition policy

Includes bibliography

Multilateral rules on competition policy: an overview of the debate

Includes bibliography

Policy competition for foreign direct investment in the Caribbean Basin: Costa Rica, the Dominican Republic and Jamaica

Includes bibliography

The effectiveness of technical assistance, socio-economic development and the absorptive capacity of competition authorities

Includes bibliography

Best practices in defence of competition in Argentina and Brazil: useful aspects for Central America

Includes bibliography

Adapted Judo training on bone-variables in postmenopausal women in pharmacological treatment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Competition, oligopoly and technological change in the construction industry: the Argentine case

Includes bibliography

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

MERCOSUR: its challenges to small and medium-sized industrial enterprises in terms of competition

Includes bibliography