We used data from a multicentre case-control study conducted in Italy between 1991 and 1994 on over 2500 cases of breast cancer and a comparable number of controls, and estimates of breast cancer incidence in Italy to compute individual breast cancer risk for Italian women. The estimated probabilities between age 50 and 80 ranged from approximately 5% (for a woman with no family history and low...

A multivariate model to assess breast cancer risk was developed by Gail et al. (M. H. Gail, L. A. Brinton, D. B. Byar, D. K. Corle, S. B. Green, C. Schairer, and J. J. Mulvihill, J. Natl. Cancer Inst., 81: 1879-1886, 1989) based on data analysis of the Breast Cancer Detection and Demonstration Project. We evaluated the model's usefulness for assigning women to risk groups for counseling and fol...

Note: The views expressed in this discussion paper are those of the author and are not necessarily those of the members or staff of the Puget Sound Regional Council. 2 Informational Paper on "Compact Growth and Adverse Health Impacts" This paper was developed by University of Washington Doctoral Student Gail Sandlin under a contract with the Puget Sound Regional Council. The scope of the contra...

Sharon Abel, PhD Jane Baran, PhD Anthony Cacace, PhD Gail Chermak, PhD$ Susan Dalebout, PhD Jay Hall 111, PhD Linda Hood, PhD Lisa Hunter, PhD James Jerger, PhD Susan Jerger, PhD Robert Keith, PhD Frank Musiek, PhD Ross Roeser, PhD Christine Sloan, PhD University of Toronto, Toronto, ON University of Massachusetts, Amherst, MA Albany Medical College, Albany, NY Washington State University, Pull...

Inductive learning techniques can be utilised to build a set of IF-THEN rules from a given example data set. This paper presents a new technique for inductive learning called GAIL (Genetic Algorithm for Inductive Learning) which is based on Genetic Algorithms. Common algorithms for inductive learning are briefly reviewed. The GAIL algorithm is described and results are shown for two benchmark d...

Imitation learning algorithms learn viable policies by imitating an expert’s behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert’s behavior is available as a fixed set of trajectories. We evaluate in terms of the expert’s cost function and observe that the distribution of trajector...

Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has potential to with high-dimensional observations as input, e.g., images. By applying a real robot, perhaps robot be obtained for daily activities like washing, folding clothes, cooking, and cleaning. However, human demonstration data are often imperfec...