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- Xin Dong, Evgeniy Gabrilovich, +6 authors Wei Zhang
- KDD
- 2014

Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase the scale even further, we need to explore automatic methods for constructing knowledge bases. Previous approaches have primarily focused on text-based extraction, which can be very… (More)

The Minimax Probability Machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound Ω. The only assumptions that MPMC makes is that good estimates of means and covariance matrixes of the classes exist. However,… (More)

- Thomas Strohmann, Gregory Z. Grudic
- NIPS
- 2002

We formulate the regression problem as one of maximizing the minimum probability, symbolized by Ω, that future predicted outputs of the regression model will be within some ±ε bound of the true regression function. Our formulation is unique in that we obtain a direct estimate of this lower probability bound Ω. The proposed framework, minimax probability… (More)

We formulate regression as maximizing the minimum probability (Ω) that the true regression function is within ±2 of the regression model. Our framework starts by posing regression as a binary classification problem, such that a solution to this single classification problem directly solves the original regression problem. Minimax probability machine… (More)

Autonomous robot navigation in unstructured outdoor environments is a challenging area of active research. At the core of this navigation task lies the concept of identifying safe, traversable paths which allow the robot to progress toward a goal. Stereo vision is frequently exploited for autonomous navigation, but has limitations in terms of its density… (More)

We formulate regression as maximizing the minimum probability (Ω) that the true regression function is within ±2 of the regression model. Our framework starts by posing regression as a binary classification problem, such that a solution to this single classification problem directly solves the original regression problem. Minimax probability machine… (More)

- Michael J. Procopio, Thomas Strohmann, +4 authors Jane Mulligan
- 2015

Autonomous robot navigation in unstructured outdoor environments is a challenging area of active research. At the core of this navigation task lies the concept of identifying safe, traversable paths which allow the robot to progress toward a goal. Stereo vision is frequently exploited for autonomous navigation, but has limitations in terms of its density… (More)

The Minimax Probability Machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound Ω. The only assumptions that MPMC makes is that good estimates of means and covariance matrixes of the classes exist. However,… (More)

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