5. A computerized method for retrieving documents, the method implemented using at least one processor and memory and comprising: searching for a set of one or more documents based on a set of one of more queries; identifying one or more of the set of documents as more probably related to the query than one or more of other of the documents using a learning machine; and defining a multi-dimensional feature vector for each candidate document, with a set of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing.
5. A computerized method for retrieving documents, the method implemented using at least one processor and memory and comprising: searching for a set of one or more documents based on a set of one of more queries; identifying one or more of the set of documents as more probably related to the query than one or more of other of the documents using a learning machine; and defining a multi-dimensional feature vector for each candidate document, with a set of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing. 11. The method of claim 5 , wherein the query is based on an input document, and each of the feature vectors is based on similarity score for one or more portions of the document and the respective one of the set of documents.