MCQMC 2012 From inference to modelling to algorithms and back again Kerrie Mengersen QUT Brisbane.
The History of Presbyterianism in the United States Part 7: Recovering Lost Ground B – “The Split Ps” and “The End of the Civil War”
HDLSS Asy’s: Geometrical Represent’n Assume, let Study Subspace Generated by Data Hyperplane through 0, ofdimension Points are “nearly equidistant to 0”,
Keshab Bahadur K.C. Bank Supervision Department Nepal Rastra Bank 1.
Dimension reduction (1) Overview PCA Factor Analysis EDR space SIR References: Applied Multivariate Analysis. kcli/sir-PHD.pdf.
Manifold Learning Dimensionality Reduction. Outline Introduction Dim. Reduction Manifold Isomap Overall procedure Approximating geodesic dist. Dijkstra’s.
Face Recognition By Sunny Tang. Outline Introduction Requirements Eigenface Fisherface Elastic bunch graph Comparison.
Pain Morning Report Robin Staib, PharmD December 22, 2011.
LDA for Lyrics Analysis CSE 291 Presentation Daryl Lim.
Epitomic Location Recognition A generative approach for location recognition K. Ni, A. Kannan, A. Criminisi and J. Winn In proc. CVPR 2008. Anchorage,
Pierre Vermaak UCT. An attempt to automate the discovery of initial solution candidates. Example-based learning Why? ◦ Track record on difficult.
Jan Kamenický. Many features ⇒ many dimensions Dimensionality reduction ◦ Feature extraction (useful representation) ◦ Classification ◦ Visualization.