The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are obtained from an optimal bijection between parts, which can provide a more valid notion of similarity. In general however, optimal assignments yield indefinite functions, which complicates their use in kernel methods. We characterize a class of base kernels used to compare parts that guarantees positive semidefinite optimal assignment kernels. These base kernels give rise to hierarchies from which the optimal assignment kernels are computed in linear time by histogram intersection. We apply these results by developing the Weisfeiler-Lehman optimal assignment kernel for graphs. It provides high classification accuracy on widely-used benchmark data sets improving over the original Weisfeiler-Lehman kernel.

This documentation is for scikit-learn version 0.18.1 -- Other versions If you use the software, please consider citing scikit-learn. Applications to real world problems with some medium sized datasets or interactive user interface. Examples illustrating the calibration of predicted probabilities of classifiers. Examples related to the sklearn.model_selection

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster Analysis we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, being able to group this data in similar groups or clusters and find hidden relationships in our data. More concretely, PCA reduces data dimensionality by finding principal components. These are the directions of maximum variation in a dataset. By reducing a dataset original features or variables to a reduced set of new ones based on the principal components, we end up with the minimum number of variables that keep the maximum amount of variation or information about how the data is distributed. If we end up with just two of these new variables, we will be able to represent each sample in our data in a two-dimensional chart (e.g. a scatterplot). As an unsupervised data analysis technique, clustering organises data samples by proximity based on its variables.

We start with a description of Lacan's work that we then take into our analytics methodology. In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the "letting the data speak" Correspondence Analysis analytics platform of Jean-Paul Benz\'ecri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu.

Embedded Intelligence, German Research Center for Artificial Intelligence, Kaiserslautern, Germany, {vitor.fortes,paul.lukowicz}@dfki.de Abstract Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognition can leverage new information sources without any, or with minimal user input. Thus, we present an approach for opportunistic activity recognition, where ubiquitous sensors lead to dynamically changing input spaces. Our method is a variation of well-established principles of machine learning, relying on unsupervised clustering to discover structure in data and inferring cluster labels from a small number of labeled dates in a semi-supervised manner. Elaborating the challenges, evaluations of over 3000 sensor combinations from three multiuser experiments are presented in detail and show the potential benefit of our approach. Keywords: Opportunistic Activity Recognition, Unsupervised Learning, Semi-supervised Learning, Classifier Adaptation 1. Introduction Today, state-of-the-art approaches to activity and context recognition typically assume fixed, narrowly defined system configurations dedicated to often also narrowly defined tasks. Such systems can only work when sensors are known in the training phase and they cannot adapt to new sensors in their environment. In turn, sensors are evermore present in our life, although not always available. When moving around, a person may face highly instrumented environments and places with little or no intelligent infrastructure. Concerning on-body sensing, a user may carry a varying collection of sensor enabled devices (mobile phone, watch, headset, etc.) on different, dynamically varying body locations (different pockets, wrist, bag). Thus, in order to realize their full potential, systems need to take advantage of devices that just "happen" to be in the environment, taking into account their current placement and relevance. In our previous work, we investigated how on-body position and orientation of on-body sensors can be inferred [1, 2], how position shifts can be tolerated [3], and how one sensor can replace another [4]. Preprint submitted to Computational Intelligence and Neuroscience March 15, 2018 integration. More precisely, this means to answer the question how can a new sensor's data be integrated in an existing activity recognition system at runtime in order to improve this recognition process. Extending a system that used n sensors to one that uses (n 1) has many challenges. For instance, training data is expensive, and thus we cannot expect the new (n 1) data to be labeled.

Over the past decade, the stellar growth of Indian economy has been challenged by persistently high levels of inflation, particularly in food prices. The primary reason behind this stubborn food inflation is mismatch in supply-demand, as domestic agricultural production has failed to keep up with rising demand owing to a number of proximate factors. The relative significance of these factors in determining the change in food prices have been analysed using gradient boosted regression trees (BRT) - a machine learning technique. The results from BRT indicates all predictor variables to be fairly significant in explaining the change in food prices, with MSP and farm wages being relatively more important than others. International food prices were found to have limited relevance in explaining the variation in domestic food prices. The challenge of ensuring food and nutritional security for growing Indian population with rising incomes needs to be addressed through resolute policy reforms.

Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in practice. Despite recent progress in proving various non-convex algorithms converge from a good initial point, it remains unclear why random or arbitrary initialization suffices in practice. We prove that the commonly used non-convex objective function for \textit{positive semidefinite} matrix completion has no spurious local minima --- all local minima must also be global. Therefore, many popular optimization algorithms such as (stochastic) gradient descent can provably solve positive semidefinite matrix completion with \textit{arbitrary} initialization in polynomial time. The result can be generalized to the setting when the observed entries contain noise. We believe that our main proof strategy can be useful for understanding geometric properties of other statistical problems involving partial or noisy observations.

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and longstanding problem in hospital management. Majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel Semi-Markov model (SMM)-based clustering scheme proposed in this paper, as opposed to clustering by admit type or condition as in previous literature. The methodology is validated by simulation and then applied to real patient data from a partner hospital where we see it outperforms current methods. Further, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM-clustering is a novel approach applicable to any temporal-spatial stochastic data that is prevalent in many industries and application areas.

Multiple predictive models are generated by using various multi-omics data types; then a final predictive model is generated by using the multiple models. Predictive models can be consolidated from various multi-omics data types, and each data type can be gathered from a various set of patients with same phenotype. Multiple data matrices of different multi-omics data types are incorporated into a large input matrix; then a predictive model is generated by using the large input matrix. It is fairly easy to leverage various machine learning methods for analyzing continuous or categorical data once a large input matrix is formed. It may be challenging to combine a large input matrix. Datasets for various multi-omics data types are first converted into intermediate forms, which are united into a large input matrix; then a predictive model is generated by using the large input matrix. Unique variables such as patient identifiers can be used to link multi-omics data types and integrate a variety of continuous or categorical data values. It may be challenging to transform into intermediate forms.

This article on going deeper into regression analysis with assumptions, plots & solutions, was posted by Manish Saraswat. Manish who works in marketing and Data Science at Analytics Vidhya believes that education can change this world. R, Data Science and Machine Learning keep him busy. Regression analysis marks the first step in predictive modeling. No doubt, it's fairly easy to implement.