PMC:4331678 / 11596-12566 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331678","sourcedb":"PMC","sourceid":"4331678","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331678","text":"MNet is different from the aforementioned approaches to integrating multiple networks in several ways. ProMK, OMG, and LIG assign weights to the individual networks solely based on their smoothness loss: the smaller the value of the smoothness loss for a network, the larger the weight assigned to this network. However, our empirical study in this paper shows that, a smaller value of the smoothness loss on an individual network does not necessarily imply that the network is a better predictor. In contrast, MNet assigns weights to the individual networks not only based on the smoothness loss, but also on the kernel-target alignment. Therefore, MNet alleviates the drawback of the existing methods. Furthermore, MNet constructs a composite network that is coherent to all the labels, whereas most multiple kernel learning algorithms optimize a composite kernel for each binary label, or optimize the composite kernel and the classifier in two separative objectives.","tracks":[]}