Latin America and the Caribbean in the World Economy 2004: 2005 trends

Includes bibliography

Latin America and the Caribbean in the World Economy 2006: 2007 trends

Includes bibliography

Latin America and the Caribbean in the World Economy 2007: 2008 trends

Includes bibliography

Tendências econômicas e sociais na América Latina e no Caribe em gráficos = Economic and social trends in Latin America and the Caribbean in graphs = Tendencias económicas y sociales en América Latina y el Caribe en gráficos

Incluye Bibliografía

Natural resources within the Union of South American Nations: status and trends for a regional development agenda

Includes bibliography

Sustainable Development in Latin America and the Caribbean: Trends, Progress, and Challenges in Sustainable Consumption and Production, Mining, Transport, Chemicals and Waste Management

Includes bibliography

International migration: Trends and institutional frameworks from the African perspective

Incluye bibliografía

Caribbean trade and integration: trends and future prospects

Includes Bibliography

Trade in services in Latin America and the Caribbean: an analysis of recent trends

Includes bibliography

Trends in United States trade with Latin America and the Caribbean and trade policy towards the region

Includes Bibliography

Demographic trends and social equity: challenges for the health sector

Includes bibliography

Violence against women in couples: Latin America and the Caribbean. A proposal for measuring its incidence and trends

Includes bibliography

Budget reform in OECD member countries: common trends

Includes bibliography

New trends in musical genre classification using optimum-path forest

Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.