The present invention relates to a method for incremental learning of a classification model, where pre-defined weak incremental learners are distributed over the distinct regions in a set of partitionings of the input domain. The partitionings and regions are organized via a binary tree and they are allowed to vary in a data-driven way, i.e., in a way to minimize the classification error rate. Moreover, to test a given data point, a mixture of decisions is obtained through the models learned in the regions that this point falls in. Hence, naturally, in the cold start phase of the data stream, the simpler models belonging to the larger regions are favored and as more data get available, the invention automatically puts more weights on the more complex models.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for incremental learning for a given data point at any time, wherein the method comprises the steps of: S101, propagating forward on the decision tree to locate a leaf node; S102, computing a node decision by, computing first a local decision at a node, then computing the node decision, if this node is non-leaf, as the weighted sum of the local decision and the node decision of the corresponding child; if this node is the leaf, as the local decision; S103, updating a local classification model; S104, updating a node classification model. i.e., the weighting coefficients; S105, updating a node partitioning model; if this is not a root node, S108, back propagating to the upper parent and go to b, otherwise, S106, returning the sign of this node decision as the class label predicts. 2. A method for incremental learning of claim 1, wherein for a given non-leaf node on the decision tree in the root-to-leaf path of a given data point xt, step S105 comprises sub steps of: computing what would be the node decision if the other child were chosen, name it as “hypothetical node decision”, and naming the decision resulted from the realized path as “realized node decision”; if the hypothetical node decision is ‘better’ than the realized node decision, then updating the node partitioning vector vp, as if the data point xt is classified with vp wrong, otherwise, updating the node partitioning vector vp, as if the data point xt is classified with vp correct. 3. The method for incremental learning of claim 1, wherein for a given non-leaf node at the decision tree in the root-to-leaf path of a given data point and a label (xt,yt), the step S104 comprises a sub step of: updating the weighting vector as if it is a classification of the input vector [this node local decision, child node decision] with respect to yt. 4. The method for incremental learning of claim 1, wherein in step S105, a path from a child of a parent node to the corresponding leaf for an observation is considered “right” or “better” if the classification accuracy at the parent node is better than what the accuracy would be if the other child was chosen. 5. The method for incremental learning of claim 1, wherein in step S105, node partitioning rules are treated as separate classification models with correct classification if the “right” path was chosen; and incorrect classification, otherwise. 6. The method for incremental learning of claim 1, wherein in step S105, the updating of node partitioning rules in a binary space partitioning tree are treated as separate sequentially learnable classification models. 7. The method for incremental learning of claim 1, wherein in step S104, the fusion of two or more node decisions are treated as separate classification models with respect to the input data label associated with observation of the node decisions.