[Reading] Support Vector Learning for Ordinal Regression

"Learning to rank" is automatically creating a ranking function that assigns scores to instances, then rank the instances by using the scores.

This paper formalizes learning to rank as a problem of binary classification, and uses SVM (support vector machine) to learn the binary classifier. This formulation minimizes pair-wise 0-1 loss.

The learned ranking function can be viewed as (1)Ranking function: given an example, output its ranking score. (2)Classifier: given a pair of instances, output their relative ranking.

[Reading] The Structure and Function of Complex Networks

This paper reviews recent work on the structure and function of networked systems such as the Internet, the World Wide Web, social networks, networks of citations between papers, and many others. The study of networks, in the form of mathematical graph theory, is one of the fundamental pillars of discrete mathematics. Networks have also been studied extensively in the social sciences.

This paper mainly describes three parts:
(1) Empirical studies of the structure of networks, including social networks, information networks, technological networks and biological networks.
(2) Some of the common properties that are observed in many of these networks, how they are measured, and why they are believed to be important for the functioning of networked systems.
(3) The mathematical modeling of networks, including random graph models and their generalizations, exponential random graphs, and Markov graphs, the small-world model and its variations, and models of growing graphs including preferential attachment models and their many variations.

[Reading] Lazy Snapping

This paper presents "Lazy Snapping", an interactive image cutout tool, also a novel coarse-to-fine UI design for image cutout. The task in image cutout is in specifying which parts of the image are "foreground" (the part you want to cut out) and which belong to the background.

Lazy Snapping consists of two steps, both are formulated as a graph cut problem:
(1) a quick object marking step
Object marking (at a coarse scale) specifies the object of interest by a few marking lines. This step is intuitive and quick for object context specification. An efficient graph cut algorithm is proposed by employing pre-computed over-segmentation so that the marking UI can provide instant visual feedback for users.
(2) a simple boundary editing step
Boundary editing (at a finer scale or on the zoomed-in image) allows the user to edit the object boundary by simply clicking and dragging polygon vertices, and use the polygon locations as soft constraints to improve snapping results around ambiguous or low contrast edges. This step is easy and efficient for accurate boundary control.