Examples of using Classification in English and their translations into Marathi
{-}
- 
                        Ecclesiastic
                    
- 
                        Computer
                    
First, the researchers“cleaned” the data by removing bogus classifications.
These classifications are not an end;
Second, after cleaning, the researchers needed to remove systematic biases in classifications.
Finally, the machine learning is used to estimate classifications for the remaining galaxies.
Second, after cleaning, the researchers needed to remove systematic biases in classifications.
The simplest way to combine classifications for each galaxy would have been to choose the most common classification.
Thus, the volunteers, in aggregate, were able to provide high-quality classifications and at a scale that the researchers could not match(Lintott et al. 2008).
Building on Galaxy Zoo, the researchers completed Galaxy Zoo 2 which collected more than 60 million more complex morphological classifications from volunteers(Masters et al. 2011).
For example, people who repeatedly classified the same galaxy- something that would happen if they were trying to manipulate the results- had all their classifications discarded.
Because very similar challenges arise in most human computation projects, it is helpful to briefly review the three steps that the Galaxy Zoo researchers used to produce their consensus classifications.
The SAP FICO module incorporates 2 noteworthy classifications of usefulness expected to run the money related records of an organization- Financials(FI) and Controlling(CO).
For example, people who repeatedly classified the same galaxy- something that would happen if they were trying to manipulate the results- had all their classifications discarded.
If this approach does not scale well, the researcher can move to a human computation project where many people contribute classifications.
These 100,000 volunteers contributed a total of more than 40 million classifications, with the majority of the classifications coming from a relatively small, core group of participants(Lintott et al. 2008).
Thus, the volunteers, in aggregate, were able to provide high quality classifications and at a scale that the researchers could not match(Lintott et al. 2008).
Using her features, her model, and the consensus Galaxy Zoo classifications, she was able to create weights on each feature, and then use these
At that point, researchers need to build second-generation systems where human classifications are used to train a machine learning model that can then be applied to virtually unlimited amounts of data.
weighting- the Galaxy Zoo research team had converted 40 million volunteer classifications into a set of consensus morphological classifications.
Together, these 100,000 volunteers contributed a total of more than 40 million classifications, with the majority of the classifications  coming from a relatively small,
More recent classifications omit Rgyalrongic, which is considered