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    • 1. 发明申请
    • DATA STORAGE WITH LOCK-FREE STATELESS PAGING CAPABILITY
    • 数据存储具有无锁定无缝封装能力
    • US20150178399A1
    • 2015-06-25
    • US14639009
    • 2015-03-04
    • W. Daniel HILLISEric BAXAugusto CALLEJASHarry KAOMathias L. KOLEHMAINEN
    • W. Daniel HILLISEric BAXAugusto CALLEJASHarry KAOMathias L. KOLEHMAINEN
    • G06F17/30
    • Disclosed are a method and apparatus for limiting the number of results returned by a store in response to a query. Upon receiving an initial query, the data store returns a page of results that includes a subset of the data items within the data store satisfying the conditions of the query. The data store also provides a marker indicating the extent of the set of data items. If a subsequent query that requests additional results which satisfy the same query conditions and that includes the marker is received, the data store returns a page of results that includes a subset of data items that are disjoint from the initial subset, and provides an updated marker which indicates the extent of the union of the initial and subsequent subsets. If still further results are desired from the data store, an additional query containing the updated marker may be submitted.
    • 公开了一种用于限制响应于查询的商店返回的结果的数量的方法和装置。 在接收到初始查询之后,数据存储返回包含符合查询条件的数据存储库内数据项子集的结果页。 数据存储还提供指示数据项集的范围的标记。 如果接收到请求满足相同查询条件并且包括标记的附加结果的后续查询,则数据存储返回包括与初始子集不相交的数据项的子集的结果页面,并提供更新的标记 这表明初始和后续子集的联合程度。 如果从数据存储还需要进一步的结果,则可以提交包含更新的标记的附加查询。
    • 4. 发明申请
    • Bounding error rate based on a worst likely assignment
    • 基于最差可能分配的边界错误率
    • US20090204559A1
    • 2009-08-13
    • US12069129
    • 2008-02-07
    • Eric BaxAugusto Callejas
    • Eric BaxAugusto Callejas
    • G06F15/18G06F11/07
    • G06N99/005G06K9/6268
    • Given a set of training examples—with known inputs and outputs—and a set of working examples—with known inputs but unknown outputs—train a classifier on the training examples. For each possible assignment of outputs to the working examples, determine whether assigning the outputs to the working examples results in a training and working set that are likely to have resulted from the same distribution. If so, then add the assignment to a likely set of assignments. For each assignment in the likely set, compute the error of the trained classifier on the assignment. Use the maximum of these errors as a probably approximately correct error bound for the classifier.
    • 给出一组具有已知输入和输出的培训示例 - 以及一组工作示例 - 具有已知输入但未知输出 - 训练培训示例上的分类器。 对于工作示例的输出的每个可能的分配,确定将输出分配给工作示例是否导致可能由相同分发产生的培训和工作集。 如果是,则将赋值分配给可能的一组分配。 对于可能的集合中的每个分配,计算分配上训练分类器的错误。 将这些错误的最大值用作分类器的大概近似正确的误差界限。