The phrase "additive learning" is correct and usable in written English.
It can be used in contexts related to education, training, or cognitive development, where learning builds upon previously acquired knowledge or skills.
Example: "In our curriculum, we emphasize additive learning, allowing students to build on their existing knowledge as they progress through the material."
Alternatives: "cumulative learning" or "incremental learning".
(He also links sugary foods and additives to learning difficulties and attention deficit disorder, although doctors remain divided on that issue).
But while these ideas for strategy combinations have empirical bases, it has not yet been established whether the benefits of the strategies to learning are additive, super-additive, or, in some cases, incompatible.
We define the additive inductive learning (AIL) as a process, in which a property y of a chemical object (such as chemical compound) consisting of s subobjects (such as atoms, bonds, fragments, etc) is approximated in an additive manner: where z ij is the value of the j-th descriptor for the i-th subobject.
Note that for a simple additive model each machine learning method has a similar area under the curve (AUC) of approximately 0.79.
He developed a phosphate-based feed additive, only to learn later that the government determined it was dangerous to cattle, he said.
During the classification stage, a new patch is first assigned to a cluster according to the closest prototype and then classified taking into account the two additive models properly learned for the cluster under consideration.
We considered four commonly used machine learning methods: stepwise additive logistic regression, artificial neural networks, support vector machines, and random forests.
In this state equation, the A matrix represents the fixed (endogenous) strength of connections between regions and the B(1)… B(m ) matrices represent the modulation of these connections by (exogenous) inputs (in this case, learning), as an additive change.
We show that an adversary can force any additive algorithm to make (N + k −1)2 mistakes in learning a monotone disjunction of at most k literals.
