The phrase "adapted means" is not correct in standard written English.
It may be intended to refer to methods or ways that have been modified or tailored for a specific purpose, but it lacks clarity and proper context.
Example: "The adapted means of communication were essential for reaching the diverse audience."
Alternatives: "modified methods" or "tailored approaches".
Other applications of prediction algorithms for the next journey of users include, for example, a recommender system for bush taxis such as suggested by Gambs et al. [152], using the predicted next location of users to recommend to pedestrians adapted means of transport that are in their neighborhood.
The function ( t, ω ) → f ( t, ω ) is β × Ϝ measurable, where β is the Borel algebra on [ 0, ∞ ) and Ϝ is the σ-algebra on Ω. f is adapted to Ϝ t, where Ϝ t is the σ-algebra generated by the random variables B ( s ) ; s ≤ t and adapted means that f is determined by B ( s ) ; s ≤ t. E [ ∫ s T f ( t, ω ) 2 d t ] < ∞.
The adapted means μ ^ ( m ^ ) and variance Σ ^ ( σ ^ 2 ) of the state output (state duration) distributions can be expressed, given the linear transformation A o (A d ) and the bias b o (b d ), under the following form: μ ^ = A o μ - b o, Σ ^ = A o Σ A o ⊤, (5) m ^ = A d m - b d, σ ^ 2 = A d σ 2 A d ⊤. (6).
Then subsequently followed the needs relating to adapted means of transportation, medical follow-up, home visit from healthcare personnel, and stimulus and motivation provided by a caregiver.
In the traditional MAP adaptation approach, the adapted mean vector of a Gaussian component can be written as: begin{array}rcl@ mathbf{M}_{c}=frac{r}{N_{c} + r}mathbf{m}_{c}+frac{N_{c}}{N_{c} + r}frac{mathbf{F}_{c}}{L} end{array} (4).
Thus, the final adapted mean vector from EPL1-based sparse speaker adaptation is given as follows: {boldsymbol{mu}}_g^{mathrm{SA}hbox mathrm{E}mathrm{P}mathrm{L}1}=signleft {boldsymbol{mu}}_g^{mathrm{ML}}-{boldsymbol{mu}}_g^{mathrm{SI}}right)odot {boldsymbol{varphi}}_g^{mathrm{SA}hbox mathrm{E}mathrm{P}mathrm{L}1}+{boldsymbol{mu}}_g^{mathrm{SI}}.
In Section 6, we analyze our experimental results on adapted mean vectors and speech recognition performance.
As can also be seen in Fig. 4, from the same ML mean vector, the differently adapted mean vectors are obtained because of the shared standard deviations.
The rest 10 % Gaussian components can be regarded as redundant ones, whose adapted mean vectors are not guaranteed to be valid.
The method proposed in this work uses a transformation matrix that computes an adapted mean and is given by μ ̂ = W ξ (49).
In the figure, the red arrows from the ML mean indicate the adapted mean vectors, and the region of non-sparse solution is surrounded by the two dashed lines.
