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<h2>Results</h2>

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<th>Group</th><td>D'yakonov</td>
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<th>Authors</th><td>D'yakonov Alexander</td>
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<th>Organization</th><td>Moscow State University, Russia</td>
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<tr><th></th><th></th></tr><tr><th colspan='2' class='results'>Competition 1, Test Set</th></tr><tr>
<th><b></b></th><th></th>
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<th>Time of submission</th><td>2005-09-28 11:04:50</td>
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<th>Method</th><td>Heuristics + Linear decision rule</td>
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<th>Accuracy</th><td>0.6772</td>
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<th>BER</th><td>0.2605</td>
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<tr><th>Confusion Matrix:  </th>
<td><table class='graybox' summary='Confusion matrix'>
<tr align='center'><td> 421 </td><td> 193 </td><td> 4 </td></tr>
<tr align='center'><td> 222 </td><td> 306 </td><td> 3 </td></tr>
<tr align='center'><td> 3 </td><td> 4 </td><td> 173 </td></tr>
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<td width='300' colspan='2'>Simple heuristics for recognition class #2 were used (number of fixations on each sentence + orders of these fixations). Linear decision rule (hyperplane) for recognition class#0/#1 was used. Features: standard + 1 auxiliary. System: Matlab 7.</td>
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<h3>Competition 1, Validation Set</h3><br/><table bgcolor='#FFFFFF' cellpadding='2' border='0' summary='Summary'><tr align='center'><th>Method</th><th>Accuracy</th><th>BER</th><th>Name</th></tr><tr align='center' class='results'><td class='results'><a href='results-16.shtml'>htsa</a></td><td class='results'><a href='results-16.shtml'>0.7224</a></td><td class='results'><a href='results-16.shtml'>0.2392</a></td><td class='results'><a href='results-16.shtml'>grozea</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-15.shtml'>kpcr-agk</a></td><td class='results'><a href='results-15.shtml'>0.7208</a></td><td class='results'><a href='results-15.shtml'>0.2352</a></td><td class='results'><a href='results-15.shtml'>Marius Popescu</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-17.shtml'></a></td><td class='results'><a href='results-17.shtml'>0.6713</a></td><td class='results'><a href='results-17.shtml'>0.2688</a></td><td class='results'><a href='results-17.shtml'>GISE</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-18.shtml'>ensemble CAN2+CV (N1-25)</a></td><td class='results'><a href='results-18.shtml'>0.6631</a></td><td class='results'><a href='results-18.shtml'>0.2953</a></td><td class='results'><a href='results-18.shtml'>kurolab</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-13.shtml'></a></td><td class='results'><a href='results-13.shtml'>0.5519</a></td><td class='results'><a href='results-13.shtml'>0.4446</a></td><td class='results'><a href='results-13.shtml'>The Tester</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-11.shtml'>Dummy Model</a></td><td class='results'><a href='results-11.shtml'>0.4794</a></td><td class='results'><a href='results-11.shtml'>0.6667</a></td><td class='results'><a href='results-11.shtml'>The Organizers</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-12.shtml'>def, nef</a></td><td class='results'><a href='results-12.shtml'>0.4703</a></td><td class='results'><a href='results-12.shtml'>0.6664</a></td><td class='results'><a href='results-12.shtml'>refu</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-14.shtml'>submission of train1_example</a></td><td class='results'><a href='results-14.shtml'>0.4185</a></td><td class='results'><a href='results-14.shtml'>0.6651</a></td><td class='results'><a href='results-14.shtml'>Test submission</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-19.shtml'>C5</a></td><td class='results'><a href='results-19.shtml'>0.3682</a></td><td class='results'><a href='results-19.shtml'>0.6677</a></td><td class='results'><a href='results-19.shtml'>cerise</a></td></tr></table><br/><br/><h3>Competition 1, Test Set</h3><br/><table bgcolor='#FFFFFF' cellpadding='2' border='0' summary='Summary'><tr align='center'><th>Method</th><th>Accuracy</th><th>BER</th><th>Name</th></tr><tr align='center' class='results'><td class='results'><a href='results-27.shtml'>MLP Ensemble (10 out of 15)</a></td><td class='results'><a href='results-27.shtml'>0.7231</a></td><td class='results'><a href='results-27.shtml'>0.2333</a></td><td class='results'><a href='results-27.shtml'>IGI_ML</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-28.shtml'>Outlier-filtered MLP ensemble</a></td><td class='results'><a href='results-28.shtml'>0.7216</a></td><td class='results'><a href='results-28.shtml'>0.2349</a></td><td class='results'><a href='results-28.shtml'>IGI_Vision</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-23.shtml'>decision trees</a></td><td class='results'><a href='results-23.shtml'>0.7141</a></td><td class='results'><a href='results-23.shtml'>0.2360</a></td><td class='results'><a href='results-23.shtml'>Michal Sapinski</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-29.shtml'>CRF</a></td><td class='results'><a href='results-29.shtml'>0.7013</a></td><td class='results'><a href='results-29.shtml'>0.2450</a></td><td class='results'><a href='results-29.shtml'>DA</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-17.shtml'>Soft grammatical inference</a></td><td class='results'><a href='results-17.shtml'>0.7013</a></td><td class='results'><a href='results-17.shtml'>0.2489</a></td><td class='results'><a href='results-17.shtml'>GISE</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-25.shtml'>ensemble CAN2(N2-8)</a></td><td class='results'><a href='results-25.shtml'>0.6990</a></td><td class='results'><a href='results-25.shtml'>0.2542</a></td><td class='results'><a href='results-25.shtml'>Shuichi Kurogi</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-15.shtml'>kpcr</a></td><td class='results'><a href='results-15.shtml'>0.6930</a></td><td class='results'><a href='results-15.shtml'>0.2500</a></td><td class='results'><a href='results-15.shtml'>Marius Popescu</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-24.shtml'>Heuristics + Linear decision rule</a></td><td class='results'><a href='results-24.shtml'>0.6772</a></td><td class='results'><a href='results-24.shtml'>0.2605</a></td><td class='results'><a href='results-24.shtml'>D'yakonov</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-21.shtml'>htsa - IFA</a></td><td class='results'><a href='results-21.shtml'>0.6539</a></td><td class='results'><a href='results-21.shtml'>0.2789</a></td><td class='results'><a href='results-21.shtml'>Cristian GROZEA</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-26.shtml'>tse-mc-lr</a></td><td class='results'><a href='results-26.shtml'>0.6072</a></td><td class='results'><a href='results-26.shtml'>0.3633</a></td><td class='results'><a href='results-26.shtml'>Tuomas Lepola</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-19.shtml'>C5</a></td><td class='results'><a href='results-19.shtml'>0.6057</a></td><td class='results'><a href='results-19.shtml'>0.3166</a></td><td class='results'><a href='results-19.shtml'>cerise</a></td></tr></table><br/><br/><h3>Competition 2, Validation Set</h3><br/><table bgcolor='#FFFFFF' cellpadding='2' border='0' summary='Summary'><tr align='center'><th>Method</th><th>Accuracy</th><th>BER</th><th>Name</th></tr><tr align='center' class='results'><td class='results'><a href='results-16.shtml'>htsa</a></td><td class='results'><a href='results-16.shtml'>0.7034</a></td><td class='results'><a href='results-16.shtml'>0.2573</a></td><td class='results'><a href='results-16.shtml'>grozea</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-11.shtml'>Dummy Model</a></td><td class='results'><a href='results-11.shtml'>0.5000</a></td><td class='results'><a href='results-11.shtml'>0.6667</a></td><td class='results'><a href='results-11.shtml'>The Organizers</a></td></tr></table><br/><br/><h3>Competition 2, Test Set</h3><br/><table bgcolor='#FFFFFF' cellpadding='2' border='0' summary='Summary'><tr align='center'><th>Method</th><th>Accuracy</th><th>BER</th><th>Name</th></tr><tr align='center' class='results'><td class='results'><a href='results-26.shtml'>4 x gnmc + lr + me</a></td><td class='results'><a href='results-26.shtml'>0.6478</a></td><td class='results'><a href='results-26.shtml'>0.3202</a></td><td class='results'><a href='results-26.shtml'>Tuomas Lepola</a></td></tr><tr align='center' bgcolor='#DCDCDC' class='results'><td class='results'><a href='results-21.shtml'>htsa - IFA</a></td><td class='results'><a href='results-21.shtml'>0.6400</a></td><td class='results'><a href='results-21.shtml'>0.2935</a></td><td class='results'><a href='results-21.shtml'>Cristian GROZEA</a></td></tr><tr align='center' class='results'><td class='results'><a href='results-28.shtml'>MLP_Ensembled</a></td><td class='results'><a href='results-28.shtml'>0.5933</a></td><td class='results'><a href='results-28.shtml'>0.3836</a></td><td class='results'><a href='results-28.shtml'>IGI_Vision</a></td></tr></table><br/><br/>

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