|Publication number||US7756802 B2|
|Application number||US 11/754,859|
|Publication date||13 Jul 2010|
|Priority date||29 May 2007|
|Also published as||US20080300833|
|Publication number||11754859, 754859, US 7756802 B2, US 7756802B2, US-B2-7756802, US7756802 B2, US7756802B2|
|Original Assignee||Microsoft Corporation|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (10), Non-Patent Citations (4), Classifications (14), Legal Events (3)|
|External Links: USPTO, USPTO Assignment, Espacenet|
Multiple base recognition modules that complement each other and a combiner are commonly used in pattern recognition for enhanced results. Combiners are usually trained from data to learn how to combine results from various base modules. The data used to train the combiner may be the same training data set used to train base modules, or a tune data set distinct from the training set. The tune set usually may provide better results as the features generated are closer to the generalization case. The effectiveness of a combiner is typically evaluated on a test data set distinct from both the training and the tune data sets.
This scheme of combiner training and evaluation provides better results when a large amount of training data is available, where base modules can be sufficiently trained, there is sufficient tune data to generate training patterns for the combiner, and there is sufficient test data to accurately evaluate the combiner precision.
However, in many practical pattern recognition problems (e.g. handwriting recognition), there may be a deficiency of training data. The aforementioned traditional method, which partitions data into a training set, a tune set, and a test set may not work well, as it may generate insufficiently trained base modules and combiners. The system is also evaluated with insufficient test patterns.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended as an aid in determining the scope of the claimed subject matter.
Embodiments are directed to employing random data partitioning to generate an infinite number of training patterns for a combiner in pattern recognition. Training data may be randomly partitioned to a training set for training based modules and a distinct tune set for generating training patterns for a combiner.
These and other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed.
As briefly described above, overall performance of a pattern recognition system may be enhanced, especially when limited amount of training data is available, by randomly partitioning the data to generate an infinite number of training patterns for a combiner distinct from a training data set for the base modules. In the following detailed description, references are made to the accompanying drawings that form a part hereof, and in which are shown by way of illustrations specific embodiments or examples. These aspects may be combined, other aspects may be utilized, and structural changes may be made without departing from the spirit or scope of the present disclosure. The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
While the embodiments will be described in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a personal computer, those skilled in the art will recognize that aspects may also be implemented in combination with other program modules.
Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that embodiments may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Embodiments may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process.
A complete pattern recognition system an input processor that gathers the input data or observations to be classified or described, a feature extraction mechanism that computes numeric or symbolic information from the observations, and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features.
The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the training set and the resulting learning strategy is characterized as supervised learning. Learning can also be unsupervised, in the sense that the system is not given an a priori labeling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns.
The classification or description scheme usually uses one of the following approaches: statistical (or decision theoretic), syntactic (or structural). Statistical pattern recognition is based on statistical characterizations of patterns, assuming that the patterns are generated by a probabilistic system. Structural pattern recognition is based on the structural interrelationships of features. A wide range of algorithms can be applied for pattern recognition, from very simple Bayesian classifiers to much more powerful neural networks.
Typical pattern recognition applications include automatic speech recognition, classification of text into several categories (e.g. spam/non-spam email messages), the automatic recognition of handwriting, or the automatic recognition of shapes (such as human faces).
As shown in
In a typical pattern recognition system, two separate processes occur: training and runtime recognition. In the training process, training data 110 is processed through input processing 116 then feature extraction 114. As a result, data models 112 are generated by learning environment 108.
In runtime recognition process, which shares processes 116 and 114 with the training process, operations begin with input processing (116) on input from a user 118 or from an information store (not shown). Depending on the pattern recognition type, the input may be speech, handwriting (ink), graphical input, and the like. For example, in a speech recognition system, an audio subsystem may convert sound input to audio signals which is then converted to textual data.
Feature extraction process 114 determines recognizable portions of the input data for processing in the learning environment 108. Learning environment 108 in conjunction with recognition engine 104 may include a number of base recognition modules and a combiner for performing the pattern recognition. Pattern recognition as performed by the recognition engine 104 in conjunction with the learning environment is the process, where input data is classified for software applications (e.g. application 106). Recognition engine 104 also loads in data models 112 (generated by training process mentioned above).
In an example speech recognition system, the recognition engine 104 may be a speech recognition engine comprising a number of components such as a language modeling module, a training module, a language model customization module, and the like. Speech recognition engine may recognize words, phrases, and the like, based on customized language and acoustic models and provides textual versions of the audio utterances. A grammar service may perform the task of managing recognized text before it is provided to applications such as a word processing application. Similar examples can be given for handwriting recognition systems, shape recognition systems, and the like.
Components of a pattern recognition system may be loaded into a server, executed over a distributed network, executed in a client device, and the like. Furthermore, the components described herein are for illustration purposes only, and do not constitute a limitation on the embodiments. A combiner training and evaluation system with random data partitioning may be implemented using fewer or additional components in various orders. Individual components may be separate applications, or part of a single application. Moreover, the pattern recognition system or its components may include individually or collectively a user interface such as a web service, a Graphical User Interface (GUI), and the like.
A method according to one embodiment may employ random data partitioning to generate any number (up to infinite) of training patterns for the combiner. The training data may be randomly partitioned to a training data set and a tune data set. Base modules may be trained using the training set. Then, the tune set (distinct from the training set) may be used to generate training patterns for the combiner based on the base modules already trained with training data set. This procedure may be repeated for every random partitioning of the training data—effectively generating infinite non-overlapping combiner training patterns even if there is limited training data for a pattern recognition system.
According to another embodiment, statistically meaningful test results may be generated for combiner evaluation without the need of a standalone test data set. After the combiner is trained as described above, different random partitioning may be used to divide training data to a training data set and a test data set. The training data set is again used to train the base modules (different from those trained during combiner training), and those base modules are then combined with the already trained combiner. Then, the system is evaluated using the new test data set. This procedure can be repeated multiple times, each providing a unique evaluation of the system. Together, the results provide a reliable statistical evaluation of the combined system without the need of a standalone test set.
According to a further embodiment, the base modules may be trained with all available training data once the combiner is already trained and evaluated providing enhanced base modules and improved overall generalization.
Referring back to
Combiner training data (i) 232 are concatenated to provide sufficient training data for combiner 240 even if the full training data is small. The base recognition modules 242 in the final recognizer 238 are then trained with the full data set, which may also be randomly partitioned into training data sets 234 and 236 to enhance the base modules, thus providing a better overall result after combining.
For each instance j of combiner evaluation 352, the training data may be randomly partitioned into training data set (j) 354 and test data set (j) 356. Training data set (j) 354 may be used to train base modules (j) 362. Combiner 364, trained as described above in conjunction with
While individual steps of the combiner training and evaluation operations may be performed by individual modules, the processing may also be performed by a single or multiple software or hardware modules, or a combination of two. The embodiments are not limited to a single software module or hardware module implementation. Any combination of software and hardware may be used for implementing combiner training and evaluation by random partitioning.
Such a system may comprise any topology of servers, clients, Internet service providers, and communication media. Also, the system may have a static or dynamic topology, where the roles of servers and clients within the system's hierarchy and their interrelations may be defined statically by an administrator or dynamically based on availability of devices, load balancing, and the like. The term “client” may refer to a client application or a client device. While a networked system implementing combiner training and evaluation with random data partitioning may involve many more components, relevant ones are discussed in conjunction with this figure.
A pattern recognition engine according to embodiments may be implemented as part of any application that utilizes pattern recognition such as speech recognition, handwriting recognition, document classification, shape recognition, and the like, in individual client devices 471-473. Parts or all of the pattern recognition system may also be implemented in server 482 and accessed from anyone of the client devices (or applications). Data stores associated with input data, training data, and the like, may be embodied in a single data store such as data store 486 or distributed over a number of data stores associated with individual client devices, servers, and the like. Dedicated database servers (e.g. database server 484) may be used to coordinate input and/or training data retrieval and storage in one or more of such data stores.
Network(s) 480 may include a secure network such as an enterprise network, an unsecure network such as a wireless open network, or the Internet. Network(s) 480 provide communication between the nodes described herein. By way of example, and not limitation, network(s) 480 may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
Many other configurations of computing devices, applications, data sources, data distribution systems may be employed to implement providing combiner training and evaluation with random data partitioning. Furthermore, the networked environments discussed in
Application 522 may be any application utilizing pattern recognition as discussed previously. The pattern recognition process may also be executed as a separate application or as an integral module of a service that provides other services to applications associated with computing device 500. Recognition engine 524 performs pattern recognition employing base modules 528 and combiner 526. According to some embodiments, random data partitioning may be employed by the recognition engine 524 to generate distinct training, tune, and test data sets from available training data for the combiner and the base modules such that reliable pattern recognition can be performed even when limited amount of training data is available. This basic configuration is illustrated in
The computing device 500 may have additional features or functionality. For example, the computing device 500 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in
The computing device 500 may also contain communication connections 516 that allow the device to communicate with other computing devices 518, such as over a wireless network in a distributed computing environment, for example, an intranet or the Internet. Other computing devices 518 may include server(s) that provide updates associated with the anti spyware service. Communication connection 516 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. The term computer readable media as used herein includes both storage media and communication media.
The claimed subject matter also includes methods of operation. These methods can be implemented in any number of ways, including the structures described in this document. One such way is by machine operations, of devices of the type described in this document.
Another optional way is for one or more of the individual operations of the methods to be performed in conjunction with one or more human operators performing some. These human operators need not be collocated with each other, but each can be only with a machine that performs a portion of the program.
Process 600 begins with operation 602, where the training data is randomly partitioned into a distinct pair of training data set and tune data set. Processing advances from operation 602 to operation 604. At operation 604, base modules are trained using the training data set. Processing continues to operation 606 from operation 604. At operation 606, the combiner is trained using training patterns generated from the tune data set that is distinct from the training data set. This procedure may be repeated for every random partitioning of the training data—effectively generating infinite non-overlapping combiner training patterns as indicated by optional operation 608 forming a loop between operations 606 and 604. Processing continues to operation 612 from operation 608.
At operation 612, the available training data is again randomly partitioned into a distinct pair of training data set and test data set. Processing continues to operation 614 from operation 612, where the training data set is again used to train the base modules (different from those trained during combiner training). Processing continues to operation 616 from operation 614.
At operation 616, the trained base modules are combined by the combiner using the test data set for testing the combiner. This procedure may also be repeated for every random partitioning of the training data—each repetition providing a unique evaluation of the system as indicated by optional operation 618 forming a loop between operations 616 and 614. After operation 616, processing moves to a calling process for further actions.
The operations included in process 600 are for illustration purposes. Providing combiner training and evaluation with random data partitioning may be implemented by similar processes with fewer or additional steps, as well as in different order of operations using the principles described herein.
The above specification, examples and data provide a complete description of the manufacture and use of the composition of the embodiments. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims and embodiments.
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|U.S. Classification||706/45, 705/29, 382/190, 704/240|
|International Classification||G06N5/00, G06F17/00|
|Cooperative Classification||G06K9/6262, G06K9/6292, G06Q10/0875, G06K9/6217|
|European Classification||G06Q10/0875, G06K9/62B11, G06K9/62B, G06K9/62F3|
|8 Jan 2008||AS||Assignment|
Owner name: MICROSOFT CORPORATION, WASHINGTON
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Owner name: MICROSOFT TECHNOLOGY LICENSING, LLC, WASHINGTON
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