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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=support+vector+machine">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=support+vector+machine]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for support vector machine]]></description>
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      <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"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-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/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/1151/SMART-An-SVM-based-Misbehavior-Detection-and-Trust-Management-Framework-for-Mobile-Ad-hoc-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/480/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-Linked-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/453/Video-Summarization-of-Laparoscopic-Cholecystectomies"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift"/>
      <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"/>
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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/357/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild.  But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy?  For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream.  The problem of responding to such changes when they are known to have occurred has been studied extensively.  In...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/352/Prediction-of-Oscar-Award-Nominations-Based-on-Movie-Scripts">
  <title><![CDATA[Prediction of Oscar Award Nominations Based on Movie Scripts]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/352/Prediction-of-Oscar-Award-Nominations-Based-on-Movie-Scripts</link>
  <description><![CDATA[According to The Numbers, the gross revenue for the Hollywood movie industry was over USD 10 billion in 2009. With annual revenues at this scale, it is critical for a movie to be successful at the box office. High revenue is closely linked to Oscar Award nominations and hence to winning of the award. Building predictive models for Oscar nominations can provide useful insights into predicting Oscar awards.  Using a movie script as a movie representation, we retrieve and weight individual movie...]]></description>
  <dc:date>2010-07-06</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/event/html/id/275/Feature-Engineering-for-Sentiment">
  <title><![CDATA[Feature Engineering for Sentiment]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/275/Feature-Engineering-for-Sentiment</link>
  <description><![CDATA[Sentiment analysis upon free text is a difficult domain since
 free text is often informally written, poorly structured, and
 rife with spelling and grammatical errors. These
 characteristics make them difficult to parse and process with
 standard language analysis tools. These factors have made
 machine learning techniques such as bag of words support vector
 machines very popular. We describe a better feature space to
 use with support vector machines that relies upon the uneven
 di...]]></description>
  <dc:date>2008-11-18</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/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems">
  <title><![CDATA[Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/659/Meerkat-Mafia-Multilingual-and-Cross-Level-Semantic-Textual-Similarity-systems</link>
  <description><![CDATA[We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference ...]]></description>
  <dc:date>2014-08-23</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/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/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/paper/html/id/480/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/paper/html/id/480/T2LD-An-automatic-framework-for-extracting-interpreting-and-representing-tables-as-Linked-Data</link>
  <description><![CDATA[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 provided by a table in the form of ...]]></description>
  <dc:date>2010-08-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/453/Video-Summarization-of-Laparoscopic-Cholecystectomies">
  <title><![CDATA[Video Summarization of Laparoscopic Cholecystectomies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/453/Video-Summarization-of-Laparoscopic-Cholecystectomies</link>
  <description><![CDATA[We compared image features with a distance metric and support vector machine to identify the critical view of a laparoscopic cholecystectomy. Our accuracy was up to 91%. We are currently experimenting with particle analysis, edge analysis, and feature clus-tering to create a more robust image classifier.]]></description>
  <dc:date>2009-11-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features">
  <title><![CDATA[Improving Binary Classification on Text Problems using Differential Word Features]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features</link>
  <description><![CDATA[We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference ...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis">
  <title><![CDATA[Delta TFIDF: An Improved Feature Space for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis</link>
  <description><![CDATA[Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using t...]]></description>
  <dc:date>2009-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild. But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy? For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream. The problem of responding to such changes when they are known to have occurred has been studied extensively. In thi...]]></description>
  <dc:date>2010-09-03</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>
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