Predictive gaussian process classification with reduced complexity

A method of generating a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, comprising:

• performing basis vector selection and adding a thus-selected basis vector to a basis vector set, including an adaptive-sampling technique that accounts for probability characteristics associated with the candidate basis vectors;
• performing hyperparameter optimization;
• controlling the basis vector selection step and hyperparameter optimization step such that the steps are alternately performed until a specified termination criteria is met;
• and
• storing the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.

A method of generating a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, comprising:

• performing basis vector selection, including considering a weighted negative-log predictive (NLP) loss measure for each example;
• performing hyperparameter optimization including considering a weighted negative-log predictive (NLP) loss measure for each example;
• controlling the basis vector selection step and hyperparameter optimization step such that the steps are alternately performed until a specified termination criteria is met;
• and
• storing the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.

A computer program product comprising at least one tangible computer-readable medium having computer program instructions tangibly embodied thereon, the computer program instructions to configure at least one computing device to generate a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, including to:

• perform basis vector selection and adding a thus-selected basis vector to a basis vector set, including to perform a margin-based method that accounts for predictive mean and variance associated with all the candidate basis vectors at that iteration;
• perform hyperparameter optimization;
• control the basis vector selection and hyperparameter optimization such that the basis vector selection and hyperparameter optimization are alternately performed until a specified termination criteria is met;
• and
• store the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.

A computer program product comprising at least one tangible computer-readable medium having computer program instructions tangibly embodied thereon, the computer program instructions to configure at least one computing device to generate a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, including to:

• perform basis vector selection and add a thus-selected basis vector to a basis vector set, including an adaptive-sampling technique that accounts for probability characteristics associated with the candidate basis vectors;
• perform hyperparameter optimization;
• control the basis vector selection step and hyperparameter optimization such that the basis vector selection step and hyperparameter optimization are alternately performed until a specified termination criteria is met;
• and
• store the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.

A computer program product comprising at least one tangible computer-readable medium having computer program instructions tangibly embodied thereon, the computer program instructions to configure at least one computing device to generate a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, including to:

• perform basis vector selection, including considering a weighted negative-log predictive (NLP) loss measure for each example;
• perform hyperparameter optimization including to consider a weighted negative-log predictive (NLP) loss measure for each example;
• control the basis vector selection and hyperparameter optimization step that the basis vector selection and hyperparameter optimization are alternately performed until a specified termination criteria is met;
• and
• store the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.

A computer system comprising at least one computing device configured to generate a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, including to:

• perform basis vector selection and adding a thus-selected basis vector to a basis vector set, including to perform a margin-based method that accounts for predictive mean and variance associated with all the candidate basis vectors at that iteration;
• perform hyperparameter optimization;
• control the basis vector selection and hyperparameter optimization such that the basis vector selection and hyperparameter optimization are alternately performed until a specified termination criteria is met;
• and
• store the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.

A computer system comprising at least one computing device configured to generate a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, including to:

• perform basis vector selection and add a thus-selected basis vector to a basis vector set, including an adaptive-sampling technique that accounts for probability characteristics associated with the candidate basis vectors;
• perform hyperparameter optimization;
• control the basis vector selection step and hyperparameter optimization such that the basis vector selection step and hyperparameter optimization are alternately performed until a specified termination criteria is met;
• and
• store the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.

A computer system comprising at least one computing device configured to generate a model of a sparse GP classifier, the classifier usable to classify examples as being either in or not in a particular category, including to:

• perform basis vector selection, including considering a weighted negative-log predictive (NLP) loss measure for each example;
• perform hyperparameter optimization including to consider a weighted negative-log predictive (NLP) loss measure for each example;
• control the basis vector selection and hyperparameter optimization step that the basis vector selection and hyperparameter optimization are alternately performed until a specified termination criteria is met;
• and
• store the selected basis vectors and optimized hyperparameters in at least one tangible computer readable medium organized in a manner to be usable as the model of the sparse GP classifier.