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active directory to RFL4ASR and then issue the command project].

Figure 47 active directory to RFL4ASR and then issue the command project].

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based on the source-forms (can be also syntax-semantic) relationships types.
based on the source-forms (can be also syntax-semantic) relationships types.
will be used as a DMM. Figure 1.2 illustrates the scope of the thesis.
will be used as a DMM. Figure 1.2 illustrates the scope of the thesis.
part of the Relative-Fuzzy membership value of an element. two separated membership functions that may work in parallel in order to calculate eac!
part of the Relative-Fuzzy membership value of an element. two separated membership functions that may work in parallel in order to calculate eac!
applicability. Figure 1.4 illustrates the strategy followed by this thesis
applicability. Figure 1.4 illustrates the strategy followed by this thesis
Figure 2.2 Definition of Data Mining as illustrated by Figure 2.2.
Figure 2.2 Definition of Data Mining as illustrated by Figure 2.2.
ANNs can be used to recognise patterns and perceive styles that are too difficult to be observed by either human or other computer skills. This is thanks to the property of
ANNs can be used to recognise patterns and perceive styles that are too difficult to be observed by either human or other computer skills. This is thanks to the property of
Figure 2.4 Typical Architecture of an ANN 48, 122, 141].
Figure 2.4 Typical Architecture of an ANN 48, 122, 141].
[102, Figure 3.2 Example of a Characteristic Function
[102, Figure 3.2 Example of a Characteristic Function
>lements are cornered in a square unit, as Figure 3.4(a) shows [63, 94]. As much as ordinary set, fuzzy sets form also n-dimensional vectors with entries in
>lements are cornered in a square unit, as Figure 3.4(a) shows [63, 94]. As much as ordinary set, fuzzy sets form also n-dimensional vectors with entries in
Figure 3.5 Examples of Membership Functions !'"*! sets, not probability of some occasion or situation [14, 28, 85, 102, 140].
Figure 3.5 Examples of Membership Functions !'"*! sets, not probability of some occasion or situation [14, 28, 85, 102, 140].
ability of fuzzy set over the crisp consider the following example.
ability of fuzzy set over the crisp consider the following example.
Figure 3.6 Crisp and Fuzzy Membership Values of Set ‘Weight Men’ both Crisp and Fuzzy types of set ‘weight men’.
Figure 3.6 Crisp and Fuzzy Membership Values of Set ‘Weight Men’ both Crisp and Fuzzy types of set ‘weight men’.
Figure 3.7 Example of a Type-2 Membership Function '**! (the blue area is the FOU)
Figure 3.7 Example of a Type-2 Membership Function '**! (the blue area is the FOU)
Figure 3.8 Type-1 and Type-2 Membership Functions **
Figure 3.8 Type-1 and Type-2 Membership Functions **
Figure 3.9 Interval Type-2 Membership Function ™ 3.4 Modal Logic and Possible- Worlds
Figure 3.9 Interval Type-2 Membership Function ™ 3.4 Modal Logic and Possible- Worlds
Figure 3.11 Venn diagram for Mathematically Modelling of Possible Worlds
Figure 3.11 Venn diagram for Mathematically Modelling of Possible Worlds
Figure 3.12 Euclidean 3D Space for Mathematically Modelling of Possible Worlds totality of all possible worlds, including non-actual worlds [74, 80].
Figure 3.12 Euclidean 3D Space for Mathematically Modelling of Possible Worlds totality of all possible worlds, including non-actual worlds [74, 80].
Table 3.2 Possible Semantic Values of Close Similar Handwriting Shapes (Syntaxes) certain interpretation-function.
Table 3.2 Possible Semantic Values of Close Similar Handwriting Shapes (Syntaxes) certain interpretation-function.
Figure 4.2 Venn diagram of Single Syntax / Single Semantic Proposition
Figure 4.2 Venn diagram of Single Syntax / Single Semantic Proposition
Figure 4.3 Venn diagram of Multi Syntax / Single Semantic Proposition
Figure 4.3 Venn diagram of Multi Syntax / Single Semantic Proposition
Figure 4.4 Venn diagram of Single Syntax/Multi Semantic Proposition
Figure 4.4 Venn diagram of Single Syntax/Multi Semantic Proposition
Figure 4.6 Proposition Representation Considering DOP 'characters' and 'digits', as illustrated in Figure 4.6.
Figure 4.6 Proposition Representation Considering DOP 'characters' and 'digits', as illustrated in Figure 4.6.
values in syntax, semantic, and DOP geometry.
values in syntax, semantic, and DOP geometry.
calculating the semantic value of the proposition.
calculating the semantic value of the proposition.
Figurer 5.1 Modifications Applied to Traditional ANN to get ML/RFL-Based Net
Figurer 5.1 Modifications Applied to Traditional ANN to get ML/RFL-Based Net
regarding domain or pragmatics) by a subnet. The abstract unit in ML/RFL-Based Net is a traditional ANN, which could be either
regarding domain or pragmatics) by a subnet. The abstract unit in ML/RFL-Based Net is a traditional ANN, which could be either
Figure 5.3 ML/RFL-Based Net and its Close Techniques 5.3.1 ML/RFL-Based Net and RF Set
Figure 5.3 ML/RFL-Based Net and its Close Techniques 5.3.1 ML/RFL-Based Net and RF Set
principle as in the traditional hierarchical multiclassifier system.
principle as in the traditional hierarchical multiclassifier system.
Table 5.2 shows.
Table 5.2 shows.
system. The callings and results are implemented as buttons, menus and scroll bars. messages). Thus, the user interface fans out information between user and RFL4ASR
system. The callings and results are implemented as buttons, menus and scroll bars. messages). Thus, the user interface fans out information between user and RFL4ASR
waves. running Get Phoneme module: extracting individual phonemes, and plots their sign
waves. running Get Phoneme module: extracting individual phonemes, and plots their sign
window that displays all phonemes files available in the Testingdata folder.
window that displays all phonemes files available in the Testingdata folder.
Figure 6.6 MFCC Computation Flow Diagram module and testing ML/RFL-Based module as the DFD of RFL4ASR shows.
Figure 6.6 MFCC Computation Flow Diagram module and testing ML/RFL-Based module as the DFD of RFL4ASR shows.
End Save both supernet and subnets Train the supernet
End Save both supernet and subnets Train the supernet
ML/RFL-Based Net type classification model under construction. Figure 6.8 Training of ML/RFL-Based Net Neural Network oecause of the possibility of the unknown number of subnets to be included in the command: ‘train’, which takes the training data and the accuracy level of matching,
ML/RFL-Based Net type classification model under construction. Figure 6.8 Training of ML/RFL-Based Net Neural Network oecause of the possibility of the unknown number of subnets to be included in the command: ‘train’, which takes the training data and the accuracy level of matching,
Net to be tested with set of phonemes. As Figure 6.9 shows, picking a ML/RFL-Based Net is achieved via loading
Net to be tested with set of phonemes. As Figure 6.9 shows, picking a ML/RFL-Based Net is achieved via loading
Figure 6.10 Testing of ML/RFL-Based Net Neural Network testing a phoneme using ML/RFL-Based Net.
Figure 6.10 Testing of ML/RFL-Based Net Neural Network testing a phoneme using ML/RFL-Based Net.
of ML/RFL-Based Net.
of ML/RFL-Based Net.
models, in general, can be considered as one of the systems generated via DM.
models, in general, can be considered as one of the systems generated via DM.
shown in Table 7.2.
shown in Table 7.2.
Table 7.2 Summary List of WEKA's Successful Classification Schemes (to be continued current state of other ASR systems).
Table 7.2 Summary List of WEKA's Successful Classification Schemes (to be continued current state of other ASR systems).
Table 7.2 Summary List of WEKA's Successful Classification Schemes
Table 7.2 Summary List of WEKA's Successful Classification Schemes
speech pattern.
speech pattern.
Each group consists of many classification schemes, Figure A.2, and a number of
Each group consists of many classification schemes, Figure A.2, and a number of
After loading the data, selecting the classification method, and setting test option,
After loading the data, selecting the classification method, and setting test option,
static system for only short-time intervals (15ms for example) [32, 92].
static system for only short-time intervals (15ms for example) [32, 92].
120], as illustrated in Figure B.2.
120], as illustrated in Figure B.2.
Related topics:
Natural Language ProcessingFuzzy Knowledge representation and reasoning
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