| Introduction | 4) Learning in Graphical Models : Michael Jordan. |
| Introduction | For example, as shown in Figure l, with the background knowledge that both Learning and Graphical models are the topics related to Machine learning, while Machine learning is the sub domain of Computer science, a human can easily determine that the two Michael Jordan in the 15t and 4th observations represent the same person. |
| Introduction | 4)[Learning]in [ Graphical Models } Michael Jordan |
| The Structural Semantic Relatedness Measure | Researcher Graphical Model W 0.28 Computer 048 Science 041 Learning |
| The Structural Semantic Relatedness Measure | For demonstration, Table 4 shows some structural semantic relatedness values of the Semantic-graph in Figure 3 (CS represents computer science and GM represents Graphical model ). |
| Introduction | In summary, our proposed model is based on the probabilistic inference of these random variables using graphical models . |
| Prior Work | In our work we use graphical models to extract context sentences. |
| Prior Work | Graphical models have a number of properties and corresponding techniques and have been used before on Information Retrieval tasks. |
| Proposed Method | A particular class of graphical models known as Markov Random Fields (MRFs) are suited for solving inference problems with uncertainty in observed data. |