90,167 research outputs found
Jet Substructure Without Trees
We present an alternative approach to identifying and characterizing jet
substructure. An angular correlation function is introduced that can be used to
extract angular and mass scales within a jet without reference to a clustering
algorithm. This procedure gives rise to a number of useful jet observables. As
an application, we construct a top quark tagging algorithm that is competitive
with existing methods.Comment: 22 pages, 16 figures, version accepted by JHE
Recommending Items in Social Tagging Systems Using Tag and Time Information
In this work we present a novel item recommendation approach that aims at
improving Collaborative Filtering (CF) in social tagging systems using the
information about tags and time. Our algorithm follows a two-step approach,
where in the first step a potentially interesting candidate item-set is found
using user-based CF and in the second step this candidate item-set is ranked
using item-based CF. Within this ranking step we integrate the information of
tag usage and time using the Base-Level Learning (BLL) equation coming from
human memory theory that is used to determine the reuse-probability of words
and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data-sets gathered
from three social tagging systems (BibSonomy, CiteULike and MovieLens) show,
the usage of tag-based and time information via the BLL equation also helps to
improve the ranking and recommendation process of items and thus, can be used
to realize an effective item recommender that outperforms two alternative
algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure
Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations
Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches
Automatic correction of part-of-speech corpora
In this study a simple method for automatic correction of part-ofspeech corpora is presented, which works as follows: Initially two or more already available part-of-speech taggers are applied on the data.
Then a sample of differing outputs is taken to train a classifier to predict for each difference which of the taggers (if any) delivered the correct output.
As classifiers we employed instance-based learning, a C4.5 decision tree and a Bayesian classifier. Their performances ranged from 59.1 % to 67.3 %. Training on the automatically corrected data finally lead to significant improvements in tagger performance
Retrieval of Boost Invariant Symbolic Observables via Feature Importance
Deep learning approaches for jet tagging in high-energy physics are
characterized as black boxes that process a large amount of information from
which it is difficult to extract key distinctive observables. In this
proceeding, we present an alternative to deep learning approaches, Boost
Invariant Polynomials, which enables direct analysis of simple analytic
expressions representing the most important features in a given task. Further,
we show how this approach provides an extremely low dimensional classifier with
a minimum set of features representing %effective discriminating physically
relevant observables and how it consequently speeds up the algorithm execution,
with relatively close performance to the algorithm using the full information
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