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As comparison, we tried to implement the mapping method on melcepstrum (MFCC) domain using MLP network proposed in[25].
Hinton and Shallice (1991) put forward a multi-layer neural network model to implement the mapping of written words onto semantic representations [see also Plaut and Shallice (1993)].
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Mappers and reducers are objects that implement the Map and Reduce methods, respectively.
For each job, the user must also provide the classes that implement the map and reduce [46] Java interfaces.
Programmers only need to implement the Map and Reduce functions while processing their massive data, and the details of computing, such as data partitioning, fault tolerance, and communication, are executed automatically by the underlying MapReduce framework.
We implemented the mapping of bnodes in two RDF graphs in two phases.
Embedded computing architectures designed for implementing the mapping and navigational algorithms must optimize the use of these resources.
We have implemented the mapping step using the Hungarian Algorithm as provided in Java by Nedas [22].
We also implemented the mapping from Petri Net to BPMN using ATL1 technology on top of two meta-models.
Comparison to the feature mapping on MFCC domain In addition, we implemented the mapping method on melcepstrum (MFCC) domain by MLP network as proposed in[25] and experimented by using position-specific training data.
The postprocessing block inputs vectors of interleaving block indices provided by the vector path and implements the mapping to a vector of (Address, Bank) pairs in accordance with (3).
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