23,614 research outputs found
Target Contrastive Pessimistic Discriminant Analysis
Domain-adaptive classifiers learn from a source domain and aim to generalize
to a target domain. If the classifier's assumptions on the relationship between
domains (e.g. covariate shift) are valid, then it will usually outperform a
non-adaptive source classifier. Unfortunately, it can perform substantially
worse when its assumptions are invalid. Validating these assumptions requires
labeled target samples, which are usually not available. We argue that, in
order to make domain-adaptive classifiers more practical, it is necessary to
focus on robust methods; robust in the sense that the model still achieves a
particular level of performance without making strong assumptions on the
relationship between domains. With this objective in mind, we formulate a
conservative parameter estimator that only deviates from the source classifier
when a lower or equal risk is guaranteed for all possible labellings of the
given target samples. We derive the corresponding estimator for a discriminant
analysis model, and show that its risk is actually strictly smaller than that
of the source classifier. Experiments indicate that our classifier outperforms
state-of-the-art classifiers for geographically biased samples.Comment: 9 pages, no figures, 2 tables. arXiv admin note: substantial text
overlap with arXiv:1706.0808
Traffic measurement and analysis
Measurement and analysis of real traffic is important to gain knowledge
about the characteristics of the traffic. Without measurement, it is
impossible to build realistic traffic models. It is recent that data
traffic was found to have self-similar properties. In this thesis work
traffic captured on the network at SICS and on the Supernet, is shown to
have this fractal-like behaviour. The traffic is also examined with
respect to which protocols and packet sizes are present and in what
proportions. In the SICS trace most packets are small, TCP is shown to be
the predominant transport protocol and NNTP the most common application.
In contrast to this, large UDP packets sent between not well-known ports
dominates the Supernet traffic. Finally, characteristics of the client
side of the WWW traffic are examined more closely. In order to extract
useful information from the packet trace, web browsers use of TCP and HTTP
is investigated including new features in HTTP/1.1 such as persistent
connections and pipelining. Empirical probability distributions are
derived describing session lengths, time between user clicks and the
amount of data transferred due to a single user click. These probability
distributions make up a simple model of WWW-sessions
Performance of Bursty World Wide Web (WWW) Sources over ABR
We model World Wide Web (WWW) servers and clients running over an ATM network
using the ABR (available bit rate) service. The WWW servers are modeled using a
variant of the SPECweb96 benchmark, while the WWW clients are based on a model
by Mah. The traffic generated by this application is typically bursty, i.e., it
has active and idle periods in transmission. A timeout occurs after given
amount of idle period. During idle period the underlying TCP congestion windows
remain open until a timeout expires. These open windows may be used to send
data in a burst when the application becomes active again. This raises the
possibility of large switch queues if the source rates are not controlled by
ABR. We study this problem and show that ABR scales well with a large number of
bursty TCP sources in the system.Comment: Submitted to WebNet `97, Toronto, November 9
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