21,007 research outputs found
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean
distance and the KL divergence, and arise throughout many areas of machine
learning. In this paper, we focus on the problem of approximating an arbitrary
Bregman divergence from supervision, and we provide a well-principled approach
to analyzing such approximations. We develop a formulation and algorithm for
learning arbitrary Bregman divergences based on approximating their underlying
convex generating function via a piecewise linear function. We provide
theoretical approximation bounds using our parameterization and show that the
generalization error for metric learning using our framework
matches the known generalization error in the strictly less general Mahalanobis
metric learning setting. We further demonstrate empirically that our method
performs well in comparison to existing metric learning methods, particularly
for clustering and ranking problems.Comment: 19 pages, 4 figure
Posthumanist Education
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ReQwip : business plan and go-to-market strategy
textThe nature of this Report is to outline the proposed business opportunity for reQwip -- an online marketplace for buying, selling and renting sports equipment -- and the go-to-market strategy for this young startup. reQwip is an Austin, Texas-based technology company founded by students and alumni of The University of Texas at Austin for the purpuse of creating a mobile, peer-to-peer (P2P) marketplace for buying, selling and renting new and used sports equipment. ReQwip is launching its minimum viable product (MVP) in Spring 2014. The MVP is a liquid marketplace focused specifically on buying and selling new and used cycling and triathlon gear in Austin,TX and greater Central Texas. This MVP is our gateway into a sporting goods industry worth 54 billion in the United States, of which $1-3 billion is used gear sales in the U.S.AdvertisingBusiness Administratio
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
New research cutting across architecture, urban studies, and psychology is
contextualizing the understanding of urban spaces according to the perceptions
of their inhabitants. One fundamental construct that relates place and
experience is ambiance, which is defined as "the mood or feeling associated
with a particular place". We posit that the systematic study of ambiance
dimensions in cities is a new domain for which multimedia research can make
pivotal contributions. We present a study to examine how images collected from
social media can be used for the crowdsourced characterization of indoor
ambiance impressions in popular urban places. We design a crowdsourcing
framework to understand suitability of social images as data source to convey
place ambiance, to examine what type of images are most suitable to describe
ambiance, and to assess how people perceive places socially from the
perspective of ambiance along 13 dimensions. Our study is based on 50,000
Foursquare images collected from 300 popular places across six cities
worldwide. The results show that reliable estimates of ambiance can be obtained
for several of the dimensions. Furthermore, we found that most aggregate
impressions of ambiance are similar across popular places in all studied
cities. We conclude by presenting a multidisciplinary research agenda for
future research in this domain
"Iâm Eating a Sandwich in Glasgow": Modeling locations with tweets
Social media such as Twitter generate large quantities of data about what a person is thinking and doing in a partic- ular location. We leverage this data to build models of locations to improve our understanding of a userâs geographic context. Understanding the userâs geographic context can in turn enable a variety of services that allow us to present information, recommend businesses and services, and place advertisements that are relevant at a hyper-local level.
In this paper we create language models of locations using coordinates extracted from geotagged Twitter data. We model locations at varying levels of granularity, from the zip code to the country level. We measure the accuracy of these models by the degree to which we can predict the location of an individual tweet, and further by the accuracy with which we can predict the location of a user. We find that we can meet the performance of the industry standard tool for pre- dicting both the tweet and the user at the country, state and city levels, and far exceed its performance at the hyper-local level, achieving a three- to ten-fold increase in accuracy at the zip code level
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