The phrase "able to classify a" is correct and usable in written English.
You can use it when discussing the capability of a system, person, or algorithm to categorize or sort items or information.
Example: "The new software is able to classify a wide range of documents based on their content."
Alternatives: "capable of categorizing a" or "able to sort a".
Multiclass classification refers to the construction of a model able to classify a response variable that can take more than two classes.
Dr Mark Young, from the University of Edinburgh, said: "It is satisfying to be able to classify a specimen that has been unexamined for more than 100 years, and doubly so to find that this discovery improves our understanding of the evolution of marine reptiles".
Overall, with Naive Bayes we are able to classify a higher percentage of individual reviews correctly, but with more variance.
The field of machine learning offers a solution to this by "training" a computer to recognise parts of an image in the same way a human does, the computer should be able to classify a noisy image unattended, potentially faster than a human operator (dependent on the computer's processing power and memory) and presenting the same output when executed repeatedly on the same input.
Of the 43 probable IC, we were able to classify a total of 22 (51.2%) cases.
The 'best case' adaptive BDI was able to classify a subject using an average of only 5.6 questions.
Interestingly, the program was able to classify an additional 89 repeats that were identified by RepeatScout in the C. elegans genome but have no close match in the C. elegans Repbase library, potentially representing novel TE families (see below).
Our task is to learn a function f : X→{0, 1} K c + K a from T that is able to classify an unlabeled data point x i (l+1≤ i≤ n) into one stage class in and to annotate it with a number of anatomical terms in at the same time.
However, a linear SVM was reliably able to classify whether a spatial pattern belonged to Sd or outcome (71.2% accuracy, p = 0.002 by one-sided binomial test against chance classification).
We present experimental results to show that ground-taken photographs are a potential source of information that can be exploited in automatic habitat classification and that our approach is able to classify with a reasonable degree of confidence four of the main habitat classes: Woodland and Scrub, Grassland and Marsh, Heathland and Miscellaneous.
Thus, the system is not able to classify with a small number of supervised samples.
