Aiming at the first two issues posted above, this section first gives our multi-instance multi-label representation for the game prop recommendation task in detail, and then formulates the game prop recommendation into a MIML learning problem together with figuring out the basic solution, which minimizes the ranking error according to props priority-role dependencies.
The present study aimed to investigate the impact of a novel hybrid algorithm consisting of Gases Brownian Motion optimization (GBMO) algorithm and the gradient based fast converging parameter estimation method on multi-instance multi-label learning.
To model those complicated dependencies, and interventions between the inputs and multiple outputs, we represent the entire log data of a player as a multi-instance example and cast the recommendation task into a multi-instance multi-label learning (MIML) problem.
There are single-instance and multi-instance applications.
In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels.
We provide details on these extensions including the transient coupling algorithm, open boundary forcing, and multi-instance sampling.
In this paper, we focus on the relationship among different duplicable (multi-instance) tasks, which are used to express and exploit data parallelism.
Multi-instance learning (MIL) is one of promising paradigms in the supervised learning aiming to handle real world classification problems where a classification target contains several featured sections, e.g., an image typically contains several salient regions.
One way to develop such systems is using the multi-instance learning (MIL) approach: a generalization of the traditional supervised learning where each example is a labeled bag that is composed of unlabeled instances, and the task is to predict the labels of unseen bags.
In [58], the MultiR system for multi-instances learning is presented, which also uses a distant supervision approach from Freebase and applies to the proposed features in [70].
Open image in new window Fig. 2 Multi-instance multi-label representation for game prop recommendation task.
