We present eecient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation of the input data distribution using Parzen windows. For recall, we obtain closed form solutions for arbitrary feedforward networks. For training, we show how the backpropagation step for an incomplete pattern can be app...

Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation...

PAC learning typically assumes that the training and test sets are drawn from the same distributions. This assumption often is violated in practice. How can machine learning algorithms be biased to output hypotheses that are robust to alterations in the test dis-tribution? We create a framework for learning in environments where the test and training distributions diier because features in the ...

Everyday computer insecurity has only gotten worse, even after many years of concerted effort . We must be missing some fundamental yet easily applicable insights into why some designs cannot be secured, how to avoid investing in them and re-creating them, and why some result in less insecurity than others . We posit that by treating valid or expected inputs to programs and network protocol sta...

Use-cases and scenarios have been identified as good inputs to generate test cases and oracles at requirement level. Yet to have an automated generation, information is missing from use cases and sequence diagrams, such as the exact inputs of the system, and the ordering constraints between the use case. The contribution of this paper is then twofold. First we propose a contract language for fu...

intelligence in animals, which are based in the capacities for self-organization in brain dynamics. Furthermore, voiceor motionactivated surveillance recorders can be triggered by prescribed inputs to limit their scope of intake, but once their gates are open, transcription is passive and representational, not active or meaningful. What is missing from passive devices is the goal-directedness c...

Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discretecontinuous data, discriminative classification with missing ...

A phone+car+cloud system can improve many vehicular scenarios significantly due to improved telemetry and the resulting optimizations. The core problem however is the inability to cope when inputs are missing or impossible to obtain apriori. We develop the concept of inference remapping which learns using correlations how to best use available substitutes for the missing inputs. We also describ...

We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the intensive care unit (ICU) of a major urban medical center, our data consists of multivariate time series of observations. The data is irregularly sampled, leading to missingness patterns in re-sampled sequen...

Statistical analysis is greatly hindered with missing information. It represents a loss of key data, but worse, it can introduce biased results in the analysis. A way to rectify the problem of missing data is to employ a sound method of imputation, a way to replace missing values with reasonable estimates. Their exists variety of estimation models like SLIM, COCOMO and other models like machine...