Combining Multi-class SVMs with Linear Ensemble Methods that Estimate the Class Posterior Probabilities Yann Guermeur LORIA-CNRS Campus Scientique, BP 239

Source URL: www.loria.fr

• Statistics
• Statistical classification
• Functions and mappings
• Support vector machine
• Convex optimization
• Function
• Convex function
• Locally convex topological vector space
• Least squares support vector machine
• Convex analysis
• Mathematical analysis
• Mathematics

Domain Adaptation in Regression Corinna Cortes1 and Mehryar Mohri2,1 1 2

Source URL: www.cs.nyu.edu

• Functions and mappings
• Convex analysis
• Convex optimization
• Mathematical optimization
• Function
• Hilbert space
• Mathematics
• Mathematical analysis
• Operator theory

Radius-Margin Bound on the Leave-One-Out Error of the LLW-M-SVM Yann Guermeur LORIA-CNRS Campus Scientifique, BPVandœuvre-l`es-Nancy cedex, France

Source URL: www.loria.fr

• Operator theory
• Hilbert space
• Statistical classification
• Linear algebra
• Support vector machine
• Reproducing kernel Hilbert space
• Function
• Convex function
• Positive-definite kernel
• Mathematics
• Mathematical analysis
• Statistics

DUALITY IN DYNAMIC DISCRETE CHOICE MODELS KHAI X. CHIONG§ , ALFRED GALICHON† , AND MATT SHUM♣ Abstract. Using results from convex analysis, we investigate a novel approach to identification and estimation of discret

Source URL: qeconomics.org

Math 205B - Topology Dr. Baez January 12, 2007 Christopher Walker QuestionA subset A of Rn is said to be star convex if for some point a0 of A, all the line segments joining a0 to other points in A lie in A.

Source URL: math.ucr.edu

Deformable Free Space Tilings for Kinetic Collision Detection Pankaj K. Agarwaly Julien Baschz Leonidas J. Guibasz John Hershbergerx

Source URL: www.cs.duke.edu

• Euclidean plane geometry
• Pseudotriangle
• Tessellation
• Simple polygon
• Polygon
• Collision detection
• Internal and external angle
• Convex and concave polygons
• Convex hull
• Geometry
• Polygons
• Triangulation

Name Midterm Exam, Econ 210A, Fall 2010 Answer as many questions as you can. Put your answers on these sheets. Question 1. Let f (x1 , x2 ) =

Source URL: econ.ucsb.edu

• Convex analysis
• Economics
• Hicksian demand function
• Marshallian demand function
• Convex function
• Quasiconvex function
• Consumer choice
• GEC
• Consumer theory
• Mathematical analysis
• Demand

Asymptotics of the 9j-symbol Introduction Hal M. Haggard and Robert G. Littlejohn A new asymptotics: the 9j-symbol

Source URL: bohr.physics.berkeley.edu

• Convex geometry
• 9J
• 6J
• Quantum gravity
• Tetrahedron
• 9-j symbol
• Geometry
• Physics
• Regge calculus

Notes on Revealed Preference Ted Bergstrom, UCSB Economics 210A November 25, 2013 We have shown how to derive demand functions, utility functions, and choice behavior, starting from some fundamental assumptions about pre

Source URL: econ.ucsb.edu

• Revealed preference
• Preference
• Utility
• Local nonsatiation
• Convex preferences
• Consumer theory
• Microeconomics
• Economics

Notes on Indirect Utility How do we show that the indirect utility function is quasi-convex? We want to show that if v(p, m) ≥ v(p0 , m0 ), then the indirect utility of the convex combination budget is worse than the i

Source URL: econ.ucsb.edu

• Operator theory
• Mathematics
• Representation theory of Lie groups
• Partial differential equations
• Spectral theory
• Mathematical analysis
• Ordinary differential equations