However, repeated observations do not have a relationship with the connectivity, and thus the performance showed no change. Figure 13 Pattern completion performance based on the study duration. (a)shows the changes in completeness and expectation for a random-order edge configuration of (2, 3). (b) shows these changes for a random-order Letrozole price edge configuration of (2, 6). … 4.3.3. Performance of Context

Expectation As a role of pattern completion, the recognition memory can be used to expect the next context in experience event stream. When a partial input enters, the memory completes missing values via the memory connectivity and generates the complete output. In this experiment, we compare the effect of online incremental learning of hypernetworks. Furthermore, the expectation performance of conventional probabilistic model, Bayesian networks, is compared. To keep up the event stream in the model, the model needs to encode all of previous data. If a model is intractable to update the new data in real time, the model has to judge and infer based on the old model. As an offline incremental hypernetwork, we set updating sections for every 1000 instances. After building a model H1off which is an offline hypernetwork encoded 1000 instances, the next 1000 instances are judged through the H1off . The tested 1000 instances are updated to H1off

so that a new model H2off is constructed. With this updating approach, the offline incremental hypernetwork is evaluated to calculate the performance of context expectation. As a controlled model, online incremental hypernetworks is compared. The model updates every instance after judging the new input data. In the experiment, three attributes are randomly selected to be a missing value. Then, the remained partial data are used as a cue to complete the missing parts via the encoded recognition memory. Figure 14 shows the change of the total ratio

of context expectation. The blue solid line shows the trend of expectation ratio of incremental memory model along with the updated instances. In the graph, there is an interesting part around 3000th instance, where the ratio decreases. It is caused by the new values in several attributes. Drug_discovery If new values in an attribute appear in the event instance, the memory cannot expect the data because there is no same value in the memory. We figure out this trend from Figures ​Figures66 and ​and77 related to the data characteristics. The red dot line that represents offline recognition memory shows a lower performance. The final performances were 29% for online model and 21% for offline model. Figure 14 The ratio of expectation performance among online (blue solid line) hypernetworks, offline (red dot line) hypernetworks, and Bayesian networks (green solid line).