Figure 3 Value range of feature function for OFDM and SC.
Another important issue for LOR is the proper design of feature function ∅ (See Methods).
For the feature-based blind recognition methods, the decision based on the selected feature function is another one major procedure.
This reveals that the feature function above may be a better choice in the classification of OFDM and SCLD signals.
In Equation 1, Recruitment is a binary classification label, d i ∈D is a forum post, and w j is a feature function of d i.
(lambda _{T}) is the importance weight of concept type T, and (f_{T}(c,d)) is a real-valued feature function.
The function f i (yc-1, y c, W, c) is a binary-valued feature function whose weight is λ i.
Considering the different features of OFDM and SCLD in two domains, we define a joint feature function to widen the distinguished gap.
Combining the features of OFDM and SCLD in the two domains, we can define a joint time-frequency domain feature function: z J = c 2 r ρ - 1 ; 0 c 2 r 0 ; ρK, (7).
As aforementioned, in LOR framework, for a given target-compound pair (T i, C ij ) a feature vector C ij = ∅(T i, C ij ) should be defined, where ∅ denotes the feature function.
To achieve high accuracy in recognizing the signal especially in low-SNR environments, the proposed method in this paper jointly exploits the features of signal in time and frequency domain, which was formulated as a unified feature function.
