Some example of the task are the zero-one loss classification where the predictor suffers a loss of one when making incorrect prediction and zero otherwise as well as the ordinal classification (also known as ordinal regression) where the predictor suffers a loss that increases as the prediction moves away from the true label. Empirical risk minimization (ERM) (Vapnik, 1992) is a standard approach for solving general multiclass classification problems by finding the classifier that minimizes a loss metric over the training data. However, since directly minimizing this loss over training data within the ERM framework is generally NPhard (Steinwart and Christmann, 2008), convex surrogate losses that can be efficiently optimized are employed to approximate the loss. Constructing surrogate losses for binary classification has been well studied, resulting in surrogate losses that enjoy desirable theoretical properties and good performance in practice. Among the popular examples are the logarithmic loss, which is minimized by the logistic regression classifier (McCullagh and Nelder, 1989), and the hinge loss, which is minimized by the support vector machine (SVM) (Boser et al., 1992; Cortes and Vapnik, 1995).

Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking/finance applications use state-of-the-art machine learning techniques to predict certain aspects of new instances. Tree ensembles, like random forests, are widely acceptable solutions on these tasks, while at the same time they are avoided due to their black-box uninterpretable nature, creating an unreasonable paradox. In this paper, we provide a sequence of actions for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An explanation provided by these systems using our approach, which we call "LionForests", can be a simple, comprehensive rule.

W e describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering. Our approach relies on several semantic similarity features based on fine-tuned word embeddings and topics similarities. In the main Subtask C, our primary submission was ranked third, with a MAP of 51.68 and accuracy of 69.94. In Subtask A, our primary submission was also third, with MAP of 77.58 and accuracy of 73.39.

To deal with datasets of different complexity, this paper presents an efficient learning model that combines the proposed Dynamic Connected Neural Decision Networks (DNDN) and a new pruning method--Dynamic Soft Pruning (DSP). DNDN is a combination of random forests and deep neural networks thereby it enjoys both the properties of powerful classification capability and representation learning functionality. Different from Deep Neural Decision Forests (DNDF), this paper adopts an end-to-end training approach by representing the classification distribution with multiple randomly initialized softmax layers, which enables the placement of the forest trees after each layer in the neural network and greatly improves the training speed and stability. Furthermore, DSP is proposed to reduce the redundant connections of the network in a soft fashion which has high flexibility but demonstrates no performance loss compared with previous approaches. Extensive experiments on different datasets demonstrate the superiority of the proposed model over other popular algorithms in solving classification tasks.

It is becoming increasingly clear that the big tech giants such as Google, Facebook, and Microsoft are extremely generous with their latest machine learning algorithms and packages (they give those away freely) because the entry barrier to the world of algorithms is pretty low right now. The open-source community and tools (such as scikit-learn) have come a long way, and plenty of open-source initiatives are propelling the vehicles of data science, digital analytics, and machine learning. Standing in 2018 we can safely say that, algorithms, programming frameworks, and machine learning packages (or even tutorials and courses how to learn these techniques) are not the scarce resource but high-quality data is. This often becomes a thorny issue on the side of the practitioners in data science (DS) and machine learning (ML) when it comes to tweaking and fine-tuning those algorithms. It will also be wise to point out, at the very beginning, that the current article pertains to the scarcity of data for algorithmic investigation, pedagogical learning, and model prototyping, and not for scaling and running a commercial operation.

This is a continuation of our series on machine learning methods that have been implemented in JASP (version 0.11 onwards). In this blog post we train a machine learning model to find clusters within our data set. The goal of a clustering task is to detect structures in the data. To do so, the algorithm needs to (1) identify the number of structures/groups in the data, and (2) figure out how the features are distributed in each group. For instance, clustering can be used to detect subgenres in electronic music, subgroups in a customer database, or to identify areas where there are greater incidences of particular types of crime.

Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. To summarize neurosurgical applications of ML where it has been compared to clinical expertise, here referred to as "natural intelligence." A systematic search was performed in the PubMed and Embase databases as of August 2016 to review all studies comparing the performance of various ML approaches with that of clinical experts in neurosurgical literature. Twenty-three studies were identified that used ML algorithms for diagnosis, presurgical planning, or outcome prediction in neurosurgical patients. Compared to clinical experts, ML models demonstrated a median absolute improvement in accuracy and area under the receiver operating curve of 13% (interquartile range 4-21%) and 0.14 (interquartile range 0.07-0.21), In 29 (58%) of the 50 outcome measures for which a P-value was provided or calculated, ML models outperformed clinical experts (P .05). In 18 of 50 (36%), no difference was seen between ML and expert performance (P .05), All 4 studies that compared clinicians assisted by ML models vs clinicians alone demonstrated a better performance in the first group. We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting.

The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also include comparisons with energy mixtures employed in electrical grids around the world. We propose including these automatically-generated Energy Usage Reports as part of standard algorithmic accountability practices, and demonstrate the use of these reports as part of model-choice in a machine learning context.

Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy. This contributes to the problem of high sample complexity, as the algorithm wastes effort by repeatedly visiting parts of the state space that have already been explored. We introduce a novel method based on Bayesian linear regression and latent space embedding to generate an intrinsic reward signal that encourages the learning agent to seek out unexplored parts of the state space. This method is computationally efficient, simple to implement, and can extend any state-of-the-art reinforcement learning algorithm. We evaluate the method on a range of algorithms and challenging control tasks, on both simulated and physical robots, demonstrating how the proposed method can significantly improve sample complexity.

The term structure of interest rates or yield curve is a function relating the interest rate with its own term. Nonlinear regression models of Nelson - Si egel and Svensson were used to estimate the yield curve using a sample of historical data supplied by th e National Stock Exchange of Costa Rica. The optimization problem involved in the estimation process of model parameters is addressed by the use of four well known combinatorial optimization metaheu-ristics: Ant colony optimization, Genetic algorithm, Part icle swarm optimization and Simulated annealing. The aim of the study is to improve the local minima obtained by a classical quasi - Newton optimization m ethod using a descent direction. Good results with at least two metaheuristics are achieved, Particle sw arm optimization and Simulated annealing.