Building extensible frameworks for data processing: The case of MDP, Modular toolkit for Data Processing

TL;DR: This thesis examines a model of the visual system, which is based on the principle of unsupervised slowness learning and using Slow Feature Analysis (SFA), and shows that biologically motivated methods like reinforcement learning are also capable of processing the high-level output from the model.

TL;DR: A framework to support spatially explicit agroecosystem modeling and data analysis over large landscape, which includes four major phases of agroECosystem simulation: simulation data preparation, site-based simulation on high performance computers, data management and dataAnalysis.

TL;DR: This chapter presents several examples of integrated environmental modeling, then explains the importance and role of spatially-explicit landscape in those efforts, and identifies the several potential research in integrated environmental system model which could further be developed in the near future using modern accelerator technologies.

TL;DR: This study proposes a three-step framework to optimize Support Vector Machine classification, automating preprocessing and parameterization, and providing a semiautomatic optimization process through integration of theoretical views, interpretation, and algorithm interfaces.

TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.

TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.

TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.

TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.