The phrase "a learning problem in" is correct and usable in written English.
It can be used when discussing issues or challenges related to the process of learning in a specific context or subject area.
Example: "The researchers identified a learning problem in mathematics that affected student performance."
Alternatives: "an educational issue in" or "a challenge in learning about".
We show how to treat sigma point placement in a UKF as a learning problem in a model based view.
The translation of a signal to its multiple representations has been reported as a learning problem in this course (Fayyaz 2014); hence, it is an important topic to address.
To summarize, we propose to cast the in silico chemogenomics problem as a learning problem in the ligand target space thus making it suitable to any classical linear machine learning approach as soon as a vector representation Φ t, c) is chosen for protein/ligand pairs.
Consider a supervised learning problem in which examples contain both numerical- and text-valued features.
These approaches transform the target fishing problem to a machine learning problem in the ligand target space.
In the presented approach, these design principles have been applied in an improved API on top of the Luigi framework and demonstrated in workflow design for a machine learning problem in drug discovery.
In a machine learning problem in which the test data are class-imbalanced, training is commonly done on class-balanced data, and class priors are then used to correct for the imbalance.
To that end, we consider protein function prediction as a structured-output learning problem in which a classifier is expected to output a totality of (interdependent) GO terms for a given sequence.
The DNA Barcode classification problem may be approached as a supervised machine learning problem in the following way [ 7]: given a reference library composed of DNA Barcode specimen sequences of known species and a collection of unknown DNA Barcode sequences (query set), recognize the latter into the species that are present in the library.
We frame protein function prediction as a structured-output learning problem in which the output space is represented by consistent subgraphs of the GO graph.
This has offered a new idea for solving machine learning problem in kernel space and is of great value in studying kernel method and machine learning.
