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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=svm">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=svm]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for svm]]></description>
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    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/402/Estimating-Temporal-Boundaries-For-Events-Using-Social-Media-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/401/A-Security-Framework-to-Cope-With-Node-Misbehaviors-in-Mobile-Ad-Hoc-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/351/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-linked-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/347/SMART-A-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/867/Named-Entity-Recognition-for-Nepali-Language"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/712/SVM-CASE-An-SVM-based-Context-Aware-Security-Framework-for-Vehicular-Ad-hoc-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/621/UMBC_EBIQUITY-CORE-Semantic-Textual-Similarity-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/546/SAT-an-SVM-based-Automated-Trust-Management-System-for-Mobile-Ad-hoc-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/548/Content-based-prediction-of-temporal-boundaries-for-events-in-Twitter"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/526/ATM-Automated-Trust-Management-for-Mobile-Ad-hoc-Networks-Using-Support-Vector-Machine"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/503/Learning-Co-reference-Relations-for-FOAF-Instances"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/506/Computing-FOAF-Co-reference-Relations-with-Rules-and-Machine-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1151/SMART-An-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/190/Detecting-Spam-Blogs-A-Machine-Learning-Approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/296/SMART-A-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/212/Splog-Blog-Dataset"/>
    </rdf:Seq>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/402/Estimating-Temporal-Boundaries-For-Events-Using-Social-Media-Data">
  <title><![CDATA[Estimating Temporal Boundaries For Events Using Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/402/Estimating-Temporal-Boundaries-For-Events-Using-Social-Media-Data</link>
  <description><![CDATA[MS Thesis Defense


Social media websites like Twitter, Flickr and YouTube generate a high volume of user generated content as a major event occurs. Our goal is to automatically determine as accurately as possible when an event starts and when it ends by analyzing the content of social media data. Estimating these temporal boundaries segments the event-related data into three major phases: the buildup to the event, the event itself, and the post-event effects and repercussions.
We describ...]]></description>
  <dc:date>2011-06-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/401/A-Security-Framework-to-Cope-With-Node-Misbehaviors-in-Mobile-Ad-Hoc-Networks">
  <title><![CDATA[A Security Framework to Cope With Node Misbehaviors in Mobile Ad Hoc Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/401/A-Security-Framework-to-Cope-With-Node-Misbehaviors-in-Mobile-Ad-Hoc-Networks</link>
  <description><![CDATA[Ph.D. Dissertation Defense

A Mobile Ad-hoc NETwork (MANET) has no fixed infrastructure, and is generally composed of a dynamic set of cooperative peers. These peers share their wireless transmission power with other peers so that indirect communication can be possible between nodes that are not in the radio range of each other . The nature of MANETs, such as node mobility, unreliable transmission medium and restricted battery power, makes them extremely vulnerable to a variety of node misb...]]></description>
  <dc:date>2011-06-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/351/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-linked-data">
  <title><![CDATA[T2LD – An automatic framework for extracting,            interpreting and representing tables as linked data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/351/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-linked-data</link>
  <description><![CDATA[MS Thesis Defense


We present an automatic framework for extracting, interpreting and
generating linked data from tables. In the process of representing
tables as linked data, we assign every column header a class label
from an appropriate ontology, link table cells (if appropriate) to an
entity from the Linked Open Data cloud and identify relations between
various columns in the table, which helps us to build an overall
interpretation of the table. Using the limited evidence provid...]]></description>
  <dc:date>2010-06-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/347/SMART-A-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks">
  <title><![CDATA[SMART: A SVM-based Misbehavior Detection and Trust Management Framework for Mobile Ad hoc Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/347/SMART-A-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks</link>
  <description><![CDATA[Due to lack of pre-deployed infrastructure, nodes in Mobile Ad hoc Networks (MANETs) are required to relay data packets for other nodes to enable multi-hop communication between nodes that are not in radio range with each other. However, whether for selfish or malicious purposes, a node may refuse to cooperate during the network operations or even attempt to interrupt them, both of which have been recognized as misbehaviors. To address the security threats caused by various misbehaviors, a SV...]]></description>
  <dc:date>2010-05-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/867/Named-Entity-Recognition-for-Nepali-Language">
  <title><![CDATA[Named Entity Recognition for Nepali Language]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/867/Named-Entity-Recognition-for-Nepali-Language</link>
  <description><![CDATA[Named Entity Recognition have been studied for different languages like English, German, Spanish and many others but no study have focused on Nepali language. In this paper we propose a neural based Nepali NER using latest state-of-the-art architecture based on grapheme-level which doesn't require any hand-crafted features and no data pre-processing. Our novel neural based model gained relative improvement of 33% to 50% compared to feature based SVM model and up to 10% improvement over state-...]]></description>
  <dc:date>2019-08-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/712/SVM-CASE-An-SVM-based-Context-Aware-Security-Framework-for-Vehicular-Ad-hoc-Networks">
  <title><![CDATA[SVM-CASE: An SVM-based Context Aware Security Framework for Vehicular Ad-hoc Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/712/SVM-CASE-An-SVM-based-Context-Aware-Security-Framework-for-Vehicular-Ad-hoc-Networks</link>
  <description><![CDATA[Vehicular Ad-hoc Networks (VANETs) are known to be very susceptible to various malicious attacks. To detect and mitigate these malicious attacks, many security mechanisms have been studied for VANETs. In this paper, we propose a context-aware security framework for VANETs that uses the Support Vector Machine (SVM) algorithm to automatically determine the boundary between malicious nodes and normal ones. Compared to existing security solutions for VANETs, the proposed framework is more resilie...]]></description>
  <dc:date>2015-09-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/621/UMBC_EBIQUITY-CORE-Semantic-Textual-Similarity-Systems">
  <title><![CDATA[UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/621/UMBC_EBIQUITY-CORE-Semantic-Textual-Similarity-Systems</link>
  <description><![CDATA[We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them used a word similarity feature that combined LSA word similarity and WordNet knowledge. The first run, which achieved the top mean score on the task of all the submissions, used a simple term alignment algorithm. The other two runs, ranked second and fourth, used SVM models to combine a larger sets of features.]]></description>
  <dc:date>2013-06-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis">
  <title><![CDATA[Identifying and Isolating Text Classification Signals from Domain and Genre Noise for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/580/Identifying-and-Isolating-Text-Classification-Signals-from-Domain-and-Genre-Noise-for-Sentiment-Analysis</link>
  <description><![CDATA[Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. This problem is generally divided into three tasks: a sentiment detection task, a topic detection task, and a sentiment measurement task. The first task attempts to determine whether the author is being objective or whether they are expressing a value judgment on the topic. The second task attempts to determine the topic of the sentiment. The third task attempts to determine whether the au...]]></description>
  <dc:date>2011-12-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/546/SAT-an-SVM-based-Automated-Trust-Management-System-for-Mobile-Ad-hoc-Networks">
  <title><![CDATA[SAT: an SVM-based Automated Trust Management System for Mobile Ad-hoc Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/546/SAT-an-SVM-based-Automated-Trust-Management-System-for-Mobile-Ad-hoc-Networks</link>
  <description><![CDATA[Mobile Ad-hoc Networks (MANETs) are extremely vulnerable to a variety of misbehaviors because of their basic features, including a lack of communication infrastructure, short transmission range, and dynamic network topology. To detect and mitigate those misbehaviors, many trust management schemes have been proposed for MANETs. Most rely on pre-defined weights to determine how each apparent misbehavior contributes to an overall measure of trustworthiness. The extremely dynamic nature of MANETs...]]></description>
  <dc:date>2011-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/548/Content-based-prediction-of-temporal-boundaries-for-events-in-Twitter">
  <title><![CDATA[Content-based prediction of temporal boundaries for events in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/548/Content-based-prediction-of-temporal-boundaries-for-events-in-Twitter</link>
  <description><![CDATA[Social media services like Twitter, Flickr and YouTube publish high volumes of user generated content as a major event occurs, making them a potential data source for event analysis. The large volume and noisy content of social media makes automatic preprocessing essential. Intuitively, the eventrelated data falls into three major phases: the buildup to the event, the event itself, and the post-event effects and repercussions.  We describe an approach to automatically determine when an antici...]]></description>
  <dc:date>2011-10-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/526/ATM-Automated-Trust-Management-for-Mobile-Ad-hoc-Networks-Using-Support-Vector-Machine">
  <title><![CDATA[ATM: Automated Trust Management for Mobile Ad-hoc Networks Using Support Vector Machine]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/526/ATM-Automated-Trust-Management-for-Mobile-Ad-hoc-Networks-Using-Support-Vector-Machine</link>
  <description><![CDATA[Mobile Ad-hoc NETworks (MANETs) are extremely susceptible to various misbehaviors, and a variety of trust management schemes have been proposed to detect and mitigate them. Most schemes rely on a set of pre-defined weights to determine how the extent of each misbehavior is used to evaluate the trustworthiness. However, due to the extremely dynamic nature of MANETs, it is not possible to determine a set of weights that are appropriate for all contexts. In this paper, an Automated Trust Managem...]]></description>
  <dc:date>2011-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/503/Learning-Co-reference-Relations-for-FOAF-Instances">
  <title><![CDATA[Learning Co-reference Relations for FOAF Instances]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/503/Learning-Co-reference-Relations-for-FOAF-Instances</link>
  <description><![CDATA[FOAF is widely used on the Web to describe people, groups and organizations and their properties. Since FOAF does not require unique IDs, it is often unclear when two FOAF instances are co-referent, i.e., denote the same entity in the world. We describe a prototype system that identifies sets of co-referent FOAF instances using logical constraints (e.g., IFPs), strong heuristics (e.g., FOAF agents described in the same file are not co-referent), and a Support Vector Machine (SVM) generated cl...]]></description>
  <dc:date>2010-11-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/506/Computing-FOAF-Co-reference-Relations-with-Rules-and-Machine-Learning">
  <title><![CDATA[Computing FOAF Co-reference Relations with Rules and Machine Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/506/Computing-FOAF-Co-reference-Relations-with-Rules-and-Machine-Learning</link>
  <description><![CDATA[The friend of a friend (FOAF) vocabulary is widely used on the Web to describe ’agents’ (people, groups and organizations) and their properties. Since FOAF does not require a unique ID for agents, it is not clear when two FOAF instances should be linked as co-referent, i.e., denote the entity in the world. One approach is to use logical constraints such as the presence of inverse functional properties as evidence that two individuals are the same. Another applies heuristics based on the s...]]></description>
  <dc:date>2010-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1151/SMART-An-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks">
  <title><![CDATA[SMART: An SVM-based Misbehavior Detection and Trust Management Framework for Mobile Ad hoc Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1151/SMART-An-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks</link>
  <description><![CDATA[Due to a lack of pre-deployed infrastructure, nodes in Mobile Ad hoc Networks (MANETs) are required to relay data packets
for other nodes to enable multi-hop communication between nodes that are not in radio range with each other. However, whether
for selfish or malicious purposes, a node may refuse to cooperate during the network operations or even attempt to interrupt them,
both of which are recognized as misbehaviors. In this paper, we describe an SVM-based Misbehavior Detection and Tru...]]></description>
  <dc:date>2010-10-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/190/Detecting-Spam-Blogs-A-Machine-Learning-Approach">
  <title><![CDATA[Detecting Spam Blogs: A Machine Learning Approach]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/190/Detecting-Spam-Blogs-A-Machine-Learning-Approach</link>
  <description><![CDATA[Weblogs or blogs are an important new way to publish information, engage in discussions, and form communities on the Internet. The Blogosphere has unfortunately been infected by several varieties of spam-like content. Blog search engines, for example, are inundated by posts from splogs – false blogs with machine generated or hijacked content whose sole purpose is to host ads or raise the PageRank of target sites. We discuss how SVM models based on local and link-based features can be used t...]]></description>
  <dc:date>2006-07-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/296/SMART-A-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks">
  <title><![CDATA[SMART: A SVM-based Misbehavior Detection and Trust Management Framework for Mobile Ad hoc Networks]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/296/SMART-A-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks</link>
  <description><![CDATA[Due to lack of pre-deployed infrastructure, nodes in Mobile Ad hoc Networks (MANETs) are required to relay data packets for other nodes to enable multi-hop communication between nodes that are not in radio range with each other. However, whether for selfish or malicious purposes, a node may refuse to cooperate during the network operations or even attempt to interrupt them, both of which have been recognized as misbehaviors. To address the security threats caused by various misbehaviors, a SV...]]></description>
  <dc:date>2010-05-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/212/Splog-Blog-Dataset">
  <title><![CDATA[Splog Blog Dataset]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/212/Splog-Blog-Dataset</link>
  <description><![CDATA[This dataset consists of 3000 blog homepages, out of which 700 have been labeled as splogs, and another 700 as authentic blogs.


This training set was used in results of three papers, with emphasis on identifying blogs [1], on detecting spam blogs [2], and on analysing the splogosphere [3].


This collection can be used in further experimenting with splogs, or for building filters that could be deployed in real world systems. We, and our academic and industrial collaborators have bee...]]></description>
  <dc:date>2006-11-14</dc:date>
 </item>
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