Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Evolving integrated multi-model framework for on line multiple time series prediction

Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much less researched problem is multiple time series prediction where the objective is to simultaneously forecast the values of multiple variables which interact with each other in time varying amounts continuously over time. In this paper we describe the use of a novel Integrated Multi-Model Framework (IMMF) that combined models developed at three di erent levels of data granularity, namely the Global, Local and Transductive models to perform multiple time series prediction. The IMMF is implemented by training a neural network to assign relative weights to predictions from the models at the three di erent levels of data granularity. Our experimental results indicate that IMMF signi cantly outperforms well established methods of time series prediction when applied to the multiple time series prediction problem

A Dynamic Embedding Model of the Media Landscape

Information about world events is disseminated through a wide variety of news channels, each with specific considerations in the choice of their reporting. Although the multiplicity of these outlets should ensure a variety of viewpoints, recent reports suggest that the rising concentration of media ownership may void this assumption. This observation motivates the study of the impact of ownership on the global media landscape and its influence on the coverage the actual viewer receives. To this end, the selection of reported events has been shown to be informative about the high-level structure of the news ecosystem. However, existing methods only provide a static view into an inherently dynamic system, providing underperforming statistical models and hindering our understanding of the media landscape as a whole. In this work, we present a dynamic embedding method that learns to capture the decision process of individual news sources in their selection of reported events while also enabling the systematic detection of large-scale transformations in the media landscape over prolonged periods of time. In an experiment covering over 580M real-world event mentions, we show our approach to outperform static embedding methods in predictive terms. We demonstrate the potential of the method for news monitoring applications and investigative journalism by shedding light on important changes in programming induced by mergers and acquisitions, policy changes, or network-wide content diffusion. These findings offer evidence of strong content convergence trends inside large broadcasting groups, influencing the news ecosystem in a time of increasing media ownership concentration

Can Carbon Sinks be Operational? An RFF Workshop Summary

An RFF Workshop brought together experts from around the world to assess the feasibility of using biological sinks to sequester carbon as part of a global atmospheric mitigation effort. The chapters of this proceeding are a result of that effort. Although the intent of the workshop was not to generate a consensus, a number of studies suggest that sinks could be a relatively inexpensive and effective carbon management tool. The chapters cover a variety of aspects and topics related to the monitoring and measurement of carbon in biological systems. They tend to support the view the carbon sequestration using biological systems is technically feasible with relatively good precision and at relatively low cost. Thus carbon sinks can be operational.carbon, sinks, global warming, sequestration, forests

"The Financial Requirements of Achieving Gender Equality and Women's Empowerment"

Although the Millennium Development Goals (MDGs) have been ratified in global and national forums, they have not yet been incorporated into operational planning within governments or international organizations. The weak link between the policies and the investments needed for their implementation is one barrier to progress. An assessment of the resources required is a critical first step in formulating and implementing strategies to achieve the MDGs. This is especially true for policies to promote gender equality and empower women. Although enough is known about such policies to implement them successfully, the costs of such interventions are not systematically calculated and integrated into country-level budgeting processes. Using country-level data, the paper estimates the costs of interventions aimed at promoting gender equality and women's empowerment in Bangladesh, Cambodia, Ghana, Tanzania, and Uganda. It then uses these estimates to calculate the costs of such interventions in other low-income countries. Finally, the paper projects the financing gap for interventions that aim directly at achieving gender equality, first for the five countries, and subsequently for all low-income countries.