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From techniques of programming such as dynamic programming underlying the basis of sequence comparison, alignment and analysis, to knowledge representation, machine learning and data mining in recent years, covering artificial neural networks (ANN), genetic algorithms, hidden Markov models (HMM), support vector machine (SVM), these have had tremendous impact in modern biology.
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The Microsoft Windows implementation of the Assembly language is nothing, but the symbolic representation of machine code.
This is expected to help pave the way for further investigations on semantics of object representation in machine learning.
This is not an optimal representation for machine learning as some algorithms assume independence between features and this is not the case.
The features presented in the previous sections are generated for each token and can be combined in various ways to create a full feature representation for machine learning.
To assess the feasibility of decision trees as a semantic structure or meaning representation for machine extraction, we examine the quality of RCT reporting, the identifiability of RCTs from abstracts, and the completeness and complexity of RCT abstracts.
Turing's original formulation of Turing Machines used the 5-tuple representation of machines.
The contributions of this work are: representation of machining states and part transfer functionality, support of multi-tasking machines in adaptive setup merging, development of special function blocks to handle sub-setups and transitions, and finally generation of function-block network for the merged setups.
Open image in new window Figure 1 Schematic representation of machining process.
Primitive studies on process tolerancing were introduced through graphical representation of machining tolerance charting (Irani et al. 1989).
As previously stated, the lack of specific techniques for generating non-photorealistic representations using machine learning was noticed, specially neural networks.
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