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We study the learnability of one-variable pattern languages in the limit with respect to the update time needed for computing a new single hypothesis and the expected total learning time taken until convergence to a correct hypothesis.
Thus, we establish the first algorithm for learning one-variable pattern languages having an expected total learning time that provably differs from the update time by a constant factor only.
The learning speed of our system is more than 2 times faster than the constellation model using Gibbs sampling, and more than 5 times faster using the Loopy Belief Propagation approach, either measured by the learning iteration number or the total learning time.
Third, using a modified version of the sequential algorithm as a subroutine, we devise a learning algorithm for one-variable patterns whose expected total learning time is O ℓ2logℓ) provided the sample strings are drawn from the target language according to a probability distribution with expected string length ℓ.
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Also, the total of learning time is greatly reduced.
Parallelization on four processor cores reduces the total learning and evaluation time by an optimal 4-fold speedup (∼13 h each for Bayesian learning and inference).
The related data are shown in Table 4. Table 4 Total of samples and learning time for object recognition Method Time cost Sample quantity Proposed 2.3 ∼10.6 min Face: 213 Car: 600 Sheep: 600 SURF_Ada [4] 47 min (face) Face: 63,000 Car: 92,00 SIFT_SVM [34] - - - Sheep: 964,849.
This process is repeated three times, giving a total learning score out of 30.
where denotes the time step of the total learning steps.
c Average Learning time.
Figure 12 demonstrates the packet loss rate decreases with the increase of learning times and the total number of packet losses for one entire learning process.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com