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Figure 4.14: Chain Link Dataset Figure 4.15: Incremental increased training size Chain Link dataset 39
Figure 4.16: different feedback sizes in batch mode for Chain Link Dataset incremental version performs better than the batch version. Breaking Ties, Least Certainty, Simple Margin and Most Certainty perform better than Random Sampling but no single ’winner’ could be found. 4.3 Datasets from UCI Machine Learning Repository Beside the generated datasets I evaluated my implementation using some datasets from the UCI Machine Learning Repository (appendix A). I used following datasets: 1. abalone 2. breast cancer (WDBC) 3. heart scale 4. hill valley 5. kr-vs-kp Detailed informations about the datasets can be found on the UCI Machine Learning Repository homepage. Again I separated the datasets into training sets for 40
Page 1 and 2: Semi-supervised Learning With Suppo
Page 3 and 4: Contents 1 Introduction 4 2 Basic D
Page 5 and 6: Chapter 1 Introduction Support Vect
Page 7 and 8: Figure 2.1: Positive samples (green
Page 9 and 10: subject to ξ i ≥ 0 y i (ω ∗
Page 11 and 12: 3.1 Random Subset Obviously if we u
Page 13 and 14: For our analysis it is important to
Page 15 and 16: the margin m − of the resulting S
Page 17 and 18: As we can see α acts as a trade-of
Page 19 and 20: where we currently look at class p
Page 21 and 22: human gets asked to label relevant
Page 23 and 24: 2. training set for semi-supervised
Page 25 and 26: < property name =" supportedPerfEva
Page 27 and 28: < property name =" comparator ">
Page 29 and 30: package name sssvm sssvm.clustermod
Page 31 and 32: Figure 4.2: Screenshot of the ssSVM
Page 33 and 34: Figure 4.4: Binary Gaussian Distrib
Page 35 and 36: Figure 4.7: Incremental increased t
Page 37 and 38: Figure 4.10: Incremental increased
Page 39: Figure 4.13: different feedback siz
Page 43 and 44: Chapter 5 Conclusion In this work I
Page 45 and 46: Bibliography [BD99] Kristin P. Benn
Page 47: [TK02] [Vap00] [VC04] [XS05] Simon

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