Databases Reference
In-Depth Information
FIGURE 11.9
Machine learning process.
words, process the same data set for multiple contexts. The limitation of this technique is that beyond
textual data its applicability is not possible. At this stage is where we bring in machine learning tech-
niques to process data, such as images, videos, graphical information, sensor data, and any other type
of data where patterns are easily discernable.
Machine learning
can be defined as a knowledge discovery and enrichment process where the
machine represented by algorithms mimic human or animal learning techniques and behaviors from
a thinking and response perspective. The biggest advantage of incorporating machine learning tech-
niques is the automation aspect of enriching the knowledge base with self-learning techniques with
minimal human intervention in the process.
Machine learning is based on a set of algorithms that can process a wide variety of data that nor-
mally is difficult to process by hand. These algorithms include:
●
Decision tree learning
●
Neural networks
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Naive Bayes
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Clustering algorithms
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Genetic algorithms
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Learning algorithms
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Explanation-based learning
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Instance-based learning
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Reinforcement-based learning
●
Support vector machines
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Associative rules
●
Recommender algorithms
The implementation of the algorithms is shown in
Figure 11.9
. The overall steps in implementing
any machine learning process are as follows:
1.
Gather the data from the inputs.
2.
Process the data through the knowledge-based learning algorithms, which observe the data
patterns and flags them for processing. The knowledge learning uses data from prior processing
