INFINITE LATENT FEATURE SELECTION

ples. In this way, we essentially subtracted a un-igram domain-specific model from our latent vari-able model in the hope that this will further reducethe domain dependence of the rest of the model pa-rameters. In preliminary experiments, this modifica-tion was beneficial for all the models incl[r]

through t he natural evolutionary process. The selected fieldsare t hen applied to the captured packets in real timethrough packet capture tool. The second component is a datapreprocessing to refine the packets for the high correctionperformance with PMAR and an HMM reduction method.PMAR is based on m[r]

tions of the words (Florian and Yarowsky, 2002).The main problem that arises with supervisedWSD techniques, including ones that do featureselection, is the paucity of labeled data. For ex-ample, the training set of SENSEVAL-2 Englishlexical sample task has only 10 labeled examplesper sense (Florian[r]

+ to ur* C-because of the relatively small number of positive examples, where C+ and C- are the penalty constants on positive and negative examples in SVMs. After that, we obtain the optimal number of tokens and the corresponding SVM parameters C- and gamma, a parameter in the radial basis kernel. I[r]

775The training stage was kept the same asMorency et al. (2007). In other words, thereis no need to change the conventional parameterestimation method on DPLVM models for adapt-ing the various inference algorithms in this paper.For more information on training DPLVMs, referto Morency et al. (2007) a[r]

tual factors. We explored a range of features usinghomogeneous and mixed classes gained by alter-native methods of feature selection. In terms off-measure on the generic class, all feature sets per-formed above the baseline(s). In the overall clas-sification, the selected sets pe[r]

Annual Conference on Computational Learning Theory: 92-100. K. Forbes-Riley and D. Litman. 2004. Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources. Proceedings of Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics ([r]

through t he natural evolutionary process. The selected fieldsare t hen applied to the captured packets in real timethrough packet capture tool. The second component is a datapreprocessing to refine the packets for the high correctionperformance with PMAR and an HMM reduction method.PMAR is based on m[r]

cancer dataset with 112 samples. The authors consider howdiffering amounts of MVs may affect classification accuracyfor a given dataset, but rather than using the true MVrate, they use the MV rate threshold (MVthld) throughouttheir study, where, for a given MVthld (MVthld= 5n%,where n= 0, 1, 2,4, 6, 8)[r]

Photoshop and use the steps in this section to see the effects of mixing the channels to create a better grayscale image. nUsing the Channels PanelThe Channels panel shown in Figure 11.8 provides access to all the channels contained in the images. To open the Channels panel, select Window ➪ Channels[r]

Note that these words are possibly ranked lower in the list than the sub-bigram because feature selection criteria (such as Chi) often prefer higher frequency terms to lower frequency ones, and every word containing the bigram certainly has a lower frequency than the bigram itself. The[r]

Hindawi Publishing CorporationEURASIP Journal on Image and Video ProcessingVolume 2010, Article ID 469563, 11 pagesdoi:10.1155/2010/469563Research ArticleFeature-Based Image Comparison for Semantic NeighborSelection in Resource-Constrained Visual Sensor NetworksYang Bai and Hairong QiDepartment of E[r]

ture selection. When implemented in Matlab 6.1 and a P41.8 GHz PC, it took about 12 hours to select 200 featuresfrom the set of training data. Figure 4 shows the first six se-lected Gabor features and locations of the 200 Gabor fea-tures on a typical face image in the database. It is interest-[r]

tionaries among multiple languages, we can con-struct a multi-partite graph based on the corre-spondence between those vocabularies, and thensmooth the PCLSA model with this graph.To show the effectiveness of PCLSA in min-ing multiple language corpus, we first construct asimulated data set based on 1[r]

NomPSP72.65 77.86 78.61 79.03Table 5: F-values for different features in a MaxEnt based Hindi NER with clustering based feature reduction[window(−m, +n) refers to the cluster or word features corresponding to previous m positions and next n posi-tions; C1 is the clusters which use sentence le[r]

2(u) by us-ing a sub-structure mining algorithm in the kernelcalculation.Third, although the kernel calculation, which uni-fies our proposed method, requires a longer train-ing time because of the feature selection, the se-lected sub-sequences have a TRIE data structure.This means a fas[r]

categories are related to the subject content, e.g., sport or education. Yang and Pedersen (1997) investigate five FS metrics and report that good FS methods improve the categorization accuracy with an aggressive feature removal using DF, IG, and CHI. More recently, Forman (2003) empirically[r]

and marginal distributions, and their direct usage of the mu-tual information measure (cf. [8, 9, 10]). One of the diffi-culties in applying MI based feature selection methods, isthe fact that evaluating the MI measure involves integrat-ing over a dense set, which leads to a computationa[r]

vised manner. Many unsupervised feature selectionalgorithms have been presented, which can be cate-gorized as feature filter (Dash et al., 2002; Talav-era, 1999) and feature wrapper (Dy and Brodley,2000; Law et al., 2002; Mitra et al., 2002; Modhaand Spangler, 2003).In this paper[r]

and the feature now at the top of the list goes through the same gain re-computing process. This heuristics comes from evidences that the gains become smaller and smaller as more and more good features are added to the model. This can be explained as follows: assume that the Maximum Likelihoo[r]