Abstract: An application-based traffic classification method for ensuring quality-of-service requirements for at least one network comprises at least one preprocessing-related step, a data classification-related step, and a learning-related step; the preprocessing-related step includes at least a windowing and sampling substep, a sub step of generating a classification dataset with labels, and a sub step of Lloyd-Max quantization, whereby an input stream is modeled as a discrete time Markov chain; the learning-related step includes at least one substep of training at least one classifier selected from a group including a classifier for a mixture of Markov components, a classifier for a k-nearest Markov component, and a classifier for a k-nearest Markov parameter; the classification-related step comprises at least one instance of application identification whereby the type of the application is determined using the trained classifier in said learning-related step.

Abstract: An online learning-based smart steering system and method comprises an online kernel perceptron classifier. The smart steering approach/method performs learning sequentially at the cloud from the entire data of multiple mesh networks, and, operates at APs for steering; both of which are executed in real-time. The system and method uses network features such as current RSSI (received signal strength indicator), current cost, target RSSI and target cost for every steering action resulting in either success of failure. The system and method uses a classifier based on this data for obtaining a model in the feature space which predicts whether a steering action can succeed or not before it is issued.

Abstract: An online learning-based smart steering system and method comprises an online kernel perceptron classifier. The smart steering approach/method performs learning sequentially at the cloud from the entire data of multiple mesh networks, and, operates at APs for steering; both of which are executed in real-time. The system and method uses network features such as current RSSI (received signal strength indicator), current cost, target RSSI and target cost for every steering action resulting in either success of failure. The system and method uses a classifier based on this data for obtaining a model in the feature space which predicts whether a steering action can succeed or not before it is issued.

Abstract: 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.

Abstract: Recurrent neural networks are powerful tools for handling incomplete data problems in machine learning thanks to their significant generative capabilities. However, the computational demand for algorithms to work in real time applications requires specialized hardware and software solutions. We disclose a method for adding recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For an incomplete data problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations.

Abstract: 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.

Abstract: Recurrent neural networks are powerful tools for handling incomplete data problems in machine learning thanks to their significant generative capabilities. However, the computational demand for algorithms to work in real time applications requires specialized hardware and software solutions. We disclose a method for adding recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For an incomplete data problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations.

Abstract: Frequency features to be used for binary classification of data using a linear classifier are selected by determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data. A mapping function is constructed for each hypothesis. The mapping functions are applied to the training data to generate frequency features, and a subset of the frequency are selecting iteratively. The linear function is then trained using the subset of frequency features and labels of the training data.

Abstract: Frequency features to be used for binary classification of data using a linear classifier are selected by determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data. A mapping function is constructed for each hypothesis. The mapping functions are applied to the training data to generate frequency features, and a subset of the frequency are selecting iteratively. The linear function is then trained using the subset of frequency features and labels of the training data.