Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given an n-vertex ighted tree with structural diameter S and a subset of m vertices,  present a technique to compute a corresponding m × m Gram matrix of the pseudoinverse of the graph Laplacian in O(n + m2 + mS) time.  discuss the application of this technique to fast label prediction on a generic graph.  approximate the graph with a spanning tree and then  predict with the kernel perceptron.  address the approximation of the graph with either a minimum spanning tree or a shortest path tree. The fast computation of the pseudoinverse enables us to address prediction problems on large graphs.  present experiments on two b-spam classification tasks, one of which includes a graph with 400,000 vertices and more than 10,000,000 edges. The results indicate that the accuracy of our technique is competitive with previous methods using the full graph information.
['Mark Herbster', 'Massimiliano Pontil', 'Sergio Rojas Galeano']
I am writing a research paper on a idea Fast Prediction on a Tree
[{"from": "human", "value": "I am writing a research paper on a idea Fast Prediction on a Tree"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given an n-vertex ighted tree with structural diameter S and a subset of m vertices,  present a technique to compute a corresponding m \u00d7 m Gram matrix of the pseudoinverse of the graph Laplacian in O(n + m2 + mS) time.  discuss the application of this technique to fast label prediction on a generic graph.  approximate the graph with a spanning tree and then  predict with the kernel perceptron.  address the approximation of the graph with either a minimum spanning tree or a shortest path tree. The fast computation of the pseudoinverse enables us to address prediction problems on large graphs.  present experiments on two b-spam classification tasks, one of which includes a graph with 400,000 vertices and more than 10,000,000 edges. The results indicate that the accuracy of our technique is competitive with previous methods using the full graph information."}]