By Demetrios Michalopoulos, Chih-Kang Hu (auth.), Tim Hendtlass, Moonis Ali (eds.)
Arti?cial Intelligence is a ?eld with an extended heritage, that is nonetheless greatly lively and constructing this day. advancements of recent and greater innovations, including the ever-increasing degrees of accessible computing assets, are fueling an expanding unfold of AI purposes. those functions, in addition to delivering the commercial intent for the learn, additionally give you the impetus to additional increase the functionality of our suggestions. This additional development at the present time is probably to return from an realizing of the methods our structures paintings, and accordingly in their obstacles, instead of from principles ‘borrowed’ from biology. From this figuring out comes development; from development comes extra program; from additional program comes the chance to extra comprehend the constraints, and so the cycle repeats itself inde?nitely. during this quantity are papers on quite a lot of issues; a few describe appli- tions which are merely attainable because of contemporary advancements, others describe new advancements in basic terms simply being moved into useful program. all of the - pers re?ect the way in which this ?eld keeps to force ahead. This convention is the fifteenth in an unbroken sequence of annual meetings on commercial and Engineering software of Arti?cial Intelligence and specialist structures prepared below the auspices of the overseas Society of utilized Intelligence.
Read Online or Download Developments in Applied Artificial Intelligence: 15th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE 2002 Cairns, Australia, June 17–20, 2002 Proceedings PDF
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Additional resources for Developments in Applied Artificial Intelligence: 15th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE 2002 Cairns, Australia, June 17–20, 2002 Proceedings
Finally, 777-dimensional feature vectors consisting of magnitude coefficients derived from mel-scale filter banks  were extracted after applying a Hamming window with a half-frame shift. The MLP consisted of 777 inputs, 35 hidden nodes, and 20 output nodes. 8, respectively. These learning parameters remained unchanged during the adaptation experiments to provide a fair comparison. 001. All experiments were carried out on a Pentium III-500 based PC with a Linux operating system. This MLP based system is then to be adapted to a new speaker through the adaptation process.
1. 001 were sorted out every learning iteration in order to skip the updating procedure for their incoming weights. As learning goes on to minimize the MSE function, the number of skipping becomes greater. Therefore, it is estimated that the computation time per iteration will be very shorten at the end of learning. The results in Table 1 show that each selective attention method produced faster convergence than the standard EBP, although the overall learning time somewhat varied depending on the speaker being adapted.
Thus, we conducted the second part of adaptation experiments where two attention criterions are integrated for MLP learning. Figure 2 shows the reduction ratio of the learning time for the integrated attention method as well as the individual two selective attention methods against the standard EBP algorithm. As expected, the integrated method performs best with its reduction ratio of more than 65% on the average. The next simulation was performed to show that our selective attention scheme could be successfully combined with other types of improved learning methods.