FEATURE SELECTION

the feature space of a multi-channel, self-paced BI system is proposed. The proposed method usesa two-stage feature selection scheme to select the most suitable movement-related potentialfeatures from the feature space. The first stage employs mutual information to filter[r]

data subset in this feature space should be stableand robust against random sampling. After deter-mination of important contextual words, we use aGaussian mixture model (GMM) based clusteringalgorithm (Bouman et al., 1998) to estimate clusterstructure and cluster number by minimizing Min-imum[r]

sometimes inconsistent. In order to better understand the relationship between these methods, building a general theoretical framework provides a fascinating perspective. Furthermore, in real applications, selecting an appropriate FS method remains hard for a new task because too many FS methods are[r]

Table 3 shows results for algorithms 1 and 4 onthe Europarl data (ep) for different devtest and testsets. Europarl data were used in all runs for train-ing and for setting the meta-parameter of numberof epochs. Testing was done on the Europarl testset and news crawl test data from the years 2010and[r]

shows the typical deviation distributions of cross-validation(i) with feature selection (solid line) and (ii) w ithout featureselection, that is, using the known best features (dashed line).In the simulations to be performed, we choose the modelssuch that the optimal feature set[r]

soft margin for SVM (C) 1000decay factor of gap (λ) 0.5threshold of χ2(τ )2.70553.8415As a result, we can calculate tree kernels with sta-tistical feature selection by using the original treekernel calculation with the sequential pattern min-ing technique introduced in this paper. More[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]

fined criteria and select only the “best” ones for the task athand. It thus may be possible to significantly reduce modeldimensions without impeding the performance of the learn-ing algorithm. In some cases one may even gain in gener-alization power by filtering irrelevant features (cf. [1]). Theneed f[r]

shown that Gabor features, when appropriately designed, areinvariant ag a inst translation, rotation, and scale [3]. Success-ful applications of Gabor filters in face recognition date backto the FERET evaluation competition [4], when the elasticbunch graph matching method [5] appeared as the winner.T[r]

we realize that GISETTE comes from an OCR problem MNIST (LeCun et al.1998), which contains 784 features of gray-level values. Thus, all features areof the same type and tend to be equally important. Our earlier experienceindicates that for such problems, SVM can handle a rather large set of fea-ture[r]

uniform framework with a sound mathematical foundation. Recent improvements on the original incremental feature selection (IFS) algorithm, such as Malouf (2002) and Zhou et al. (2003), greatly speed up the feature selection process. However, like many other statistical mo[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]

dialogue data. We have given an algorithm that increased the size of the training set producing even better accuracy than the manually labeled training set, until it fell behind due to its inability to add more than 58 examples. We have shown the positive effect of selecting a good set of features o[r]

and Cabrera et al. [3] deal with statistical methods forintrusion detection. Lee and Xiang’s research [4]isabouttheoretical measures for anomaly detection, and Ryan [5]uses artificial neural networks with supervised learning. Incontrast, unsupervised schemes make appropriate labels fora given dataset[r]

and Cabrera et al. [3] deal with statistical methods forintrusion detection. Lee and Xiang’s research [4]isabouttheoretical measures for anomaly detection, and Ryan [5]uses artificial neural networks with supervised learning. Incontrast, unsupervised schemes make appropriate labels fora given dataset[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]

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]

redundant with regard to the bigram features as-sociated to them. Similarly, according to the sec-ond issue addressed, a bigram might cover for more than one word-bigram. For instance, the bigram “篇小” is a sub-bigram of the word-bigrams (phrases) “短篇小说(short story)”, “中篇小说(novelette)”, “长[r]

accuracy but saves time in the learning process.This Chapter provides survey of feature selection techniques and variable selec-tion techniques5.2 Feature Selection Techniques5.2.1 Feature FiltersThe earliest approaches to feature selection within mac[r]

using all features, with the better correlation between the lat-ter two showing no general trend, but differing for differentmodels.This is not the first time that concerns have been raisedregarding the microarray classification paradigm. These con-cerns go back to practically the outset of the expressi[r]