The first two chapters of this thesis investigate a possibly fundamental aspect of technological progress. If knowledge accumulates as technology progresses, then successive generations of innovators may face an increasing educational burden, forcing them to spend longer periods in education…

The use of new technologies does not mean that the applied education model is modern. New technologies can be used in a way that follows the traditional education model, with all its deficiencies. The collaborative education model involves students in…

Co-training can learn from datasets having a small number of labelled examples and a large number of unlabelled ones. It is an iterative algorithm where examples labelled in previous iterations are used to improve the classification of examples from the…

Software architecture constitutes the primary design of a software system. Consequently, architectural design decisions involved in architecture design have a key impact on the system in such aspects as future maintenance costs, resulting quality, and timeliness. However, the applied knowledge…

by Leslie Prince Rudolph., Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1999., Includes bibliographical references (leaves 70-72).

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1990., Includes bibliographical references (leaves 231-234).

by Vuong P. Nguyen., Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997., Includes bibliographical references (leaves 67-68).

2013

Knowledge of the distribution of soil organic matter (SOM) fractions is important in managing soils toward a sustainable agricultural system ina tropical environment, However, data on Histosols in limited. This study developed 19 regions of Brazil. Soil organic matter was…

I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific…