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Table 1 Feature vector

in User Model User-Adap Inter DOI 10.1007/s11257-007-9035-8 ORIGINAL PAPER
by Lampropoulos George, A. Tsihrintzis 2007
"... In PAGE 9: ... The pitch content features typically include the amplitudes and periods of maximum peaks in the histogram, pitch intervals between the two most prominent peaks, and the overall sums of the histograms. Table1 summarizes the objective feature vector description. 4 MUSIPER architecture and module description 4.... ..."

Tabla 6. Construction of the Enhanced Feature Set

in Authorship Attribution using Word Sequences
by Rosa María Coyotl-morales, Luis Villaseñor-pineda, Manuel Montes-y-gómez, Paolo Rosso, Laboratorio De Tecnologías Del Lenguaje, Informáticos Computación

Table 1. The instances of the feature vector

in USAID: Unifying Signature-Based and Anomaly-Based Intrusion Detection
by Zhuowei Li, Amitabha Das, Jianying zhou
"... In PAGE 3: ...2 An Illustrative Example Let us assume that FV = {F1,F2,F3}. The example instances of the feature vector are listed below in Table1 . The feature ranges for every feature are listed as follows.... ..."

Table 1 Description of the feature vector

in Abstract Reconstructing shredded documents through
by Feature Matching, Edson Justino A, Luiz S. Oliveira A, Cinthia Freitas A 2005
"... In PAGE 4: ... Such distances are achieved by means of the well-known Eucli- dean distance. Table1 describes the feature vector extracted from the polygon depicted in Fig. 5.... ..."

Table 2. Count Feature Vectors

in Feature Selection for Temporal Health Records
by Rohan Baxter Graham, Graham J. Williams, Hongxing He 2001
"... In PAGE 4: ... The original sequence data, as shown in Fig. 1, is mapped to the features shown in Table2 . This... ..."
Cited by 1

Table 1: Feature vector illustration.

in A supervised learning approach for routing optimizations in wireless sensor networks
by Yong Wang, Margaret Martonosi, Li-shiuan Peh
"... In PAGE 4: ... Some of them are related to channel conditions, some of them related to network congestion, and some of them to both. Table1 lists the features we collected for link quality learn- ing. They are all numerical values.... ..."

Table 4: An example of feature vector.

in CEFLE and Direkt Profil: A new computer learner corpus in French L2 and a system for grammatical profiling
by Jonas Granfeldt, Pierre Nugues, Malin Ågren, Jonas Thulin, Emil Persson, Suzanne Schlyter 2006
"... In PAGE 4: ...atabase (New et al., 2004) . In total, 33 features were used in the training session. These are presented in Table4 with their respective values for this particular learner text. Figure 1 shows an example of a resulting decision tree for classifying learner texts according to their developmental stage.... ..."
Cited by 1

Table 2: Structure of feature vector.

in Multiscale Branch and Bound Image Database Search
by Jau-Yuen Chen Charles, Charles A. Bouman, Jan P. Allebach 1997
"... In PAGE 7: ...pponent color space. In fact, Gargi et. al.11 evaluated six color spaces, and found that La b performed well. Table2 shows that we chose the number of histogram bins so that the resolution of each bin is approximately 6 E. In addition, we found that it was important to smooth the histogram so that small color shifts do not excessively e ect the match.... In PAGE 7: ... The values of a and b are computed similarly. These values are then histogramed as shown in Table2 . Notice that a 17th bin is reserved for the case of = ;.... ..."
Cited by 14

Table 7: Our chunk prediction results: with enhanced features

in Text Chunking based on a Generalization of Winnow
by Tong Zhang, Fred Damerau, David Johnson 2002
"... In PAGE 15: ... This simplicity allows other researchers to reproduce our results easily. In Table7 , we report the results of our system with the basic features enhanced by using ESG syntactic roles, showing that using more linguistic features can enhance the per- formance of the system. In addition, since regularized Winnow is able to pick up relevant features automatically, we can easily integrate different features into our system in a sys- tematic way without concerning ourselves with the semantics of the features.... In PAGE 18: ... The second type of error is certainly caused by non-optimal feature construction. By comparing results corresponding to first order features only as in Table 9, basic features as in Table 6, and enhanced features as in Table7 , it is clear that appropriate feature choices can have significant impact on the system performance. We observe that some of the chunk... ..."
Cited by 30

TABLE IV FEATURE VECTORS FOR THE AIS

in A cooperative ais framework for intrusion detection
by Katja Luther, Rainer Bye, et al. 2007
Cited by 1
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