Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous for pattern recognition, with support vector machines (SVMs) being the most well-known method for classification problems. However, there are limitations to the successful solution to such problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element to computational speed-ups afforded by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here, we propose and use two novel methods which represent the feature space of a classification problem by a quantum state, taking advantage of the large dimensionality of quantum Hilbert space to obtain an enhanced solution. One method, the quantum variational classifier builds on [1,2] and operates through using a variational quantum circuit to classify a training set in direct analogy to conventional SVMs. In the second, a quantum kernel estimator, we estimate the kernel function and optimize the classifier directly. The two methods present a new class of tools for exploring the applications of noisy intermediate scale quantum computers to machine learning.

Abstract--This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos. We designed four base regression models using visual and audio features, and then used a spectral approach to fuse them to obtain improved performance. (IEEE WCCI 2018). The dataset was composed of 420 relatively long emotion videos with an average length of 1 minute, collected from a variety of Youtube channels. Videos were separated into clips based on utterances, and each utterance's valence and arousal levels were annotated by at least five independent subjects using the Amazon Mechanical Turk tool.

Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, broadly known as unsupervised learning. Support vector clustering algorithm is a well-known clustering algorithm based on support vector machines and Gaussian kernels. In this paper, we have investigated the support vector clustering algorithm in quantum paradigm. We have developed a quantum algorithm which is based on quantum support vector machine and the quantum kernel (Gaussian kernel and polynomial kernel) formulation. The investigation exhibits approximately exponential speed up in the quantum version with respect to the classical counterpart.

In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over the state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO are interpretable as outlier scores and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting - flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task. 1 Introduction Many applications are modeled and analyzed as attributed networks, where vertices represent entities with attributes and edges express the interactions or relationships between entities. In many scenarios, one also has knowledge about the labels of some vertices in an attributed network. Such networks are referred to as partially labeled attributed networks (PLANs). While PLANs contain much richer information than plain networks, they are also more challenging to analyze.

Iris, the part of the eye responsible for controlling the amount of light entering into it, has been a subject of psychological interest for centuries. Progressing from physiology, literature and poetry, eyes are being used in neuro-linguistic programming (NLP), which focuses on interactions of the human body, iris movements and positions. Basically, NLP is being used to gain focus on assessing human behaviour and mental activities. Lately, machine learning has also made its way into psychology-related issues.

In this paper we measured the stability of stochastic gradient method (SGM) for learning an approximated Fourier primal support vector machine. The stability of an algorithm is considered by measuring the generalization error in terms of the absolute difference between the test and the training error. Our problem is to learn an approximated kernel function using random Fourier features for a binary classification problem via online convex optimization settings. For a convex, Lipschitz continuous and smooth loss function, given reasonable number of iterations stochastic gradient method is stable. We showed that with a high probability SGM generalizes well for an approximated kernel under given assumptions.We empirically verified the theoretical findings for different parameters using several data sets.

"We noticed that most patients who were dissatisfied with the displayed waiting times were delayed for longer than predicted, so the need for more accurate models became imminent," Curtis et al. said. "We also wanted to predict not only waiting times for walk-in facilities, but also delays for the scheduled facilities." Stepping outside of existing research, Curtis and her co-authors zoned in on machine learning, an artificial intelligence modality that can reflect sophisticated trends otherwise difficult to capture with traditional regression approaches. Machine learning models can also resist noise, adapt to changing environments and run without human supervision, the researchers wrote, which fit the needs of a waiting room to a T. The team considered CT exams, MRI, ultrasound and radiography--only the last of which offered walk-in appointments--for the study. They evaluated 10 machine learning algorithms, including neural network, random forest, support vector machine, elastic net, multivariate adaptive regression splines, k-th nearest neighbor and linear regression, to find the algorithm that most closely predicted waiting times.

"We noticed that most patients who were dissatisfied with the displayed waiting times were delayed for longer than predicted, so the need for more accurate models became imminent," Curtis et al. said. "We also wanted to predict not only waiting times for walk-in facilities, but also delays for the scheduled facilities." Stepping outside of existing research, Curtis and her co-authors zoned in on machine learning, an artificial intelligence modality that can reflect sophisticated trends otherwise difficult to capture with traditional regression approaches. Machine learning models can also resist noise, adapt to changing environments and run without human supervision, the researchers wrote, which fit the needs of a waiting room to a T. The team considered CT exams, MRI, ultrasound and radiography--only the last of which offered walk-in appointments--for the study. They evaluated 10 machine learning algorithms, including neural network, random forest, support vector machine, elastic net, multivariate adaptive regression splines, k-th nearest neighbor and linear regression, to find the algorithm that most closely predicted waiting times.

When I was waking up at 6 AM to study Support Vector Machines I thought: "This is really tough! But, hey, at least I will become very valuable for my future employer!". If I could get the DeLorean, I would go back in time and call "Bulls**t!" on myself. The truth is that reality is much more nuanced, and the fact the field is still far away from being mature isn't helping at all. The classical story goes something like this: "data scientists spend 80% of their time getting, cleaning and managing data, only the rest is spent on analysis and machine learning".

There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilizes a rank-one tensor to capture the data structure, thereby alleviating the overfitting and curse of dimensionality problems in the conventional support vector machine (SVM). However, the expressive power of a rank-one tensor is restrictive for many real-world data. To overcome this limitation, we introduce a support tensor train machine (STTM) by replacing the rank-one tensor in an STM with a tensor train. Experiments validate and confirm the superiority of an STTM over the SVM and STM. 1 Introduction Classification algorithm design has been a popular topic in machine learning, pattern recognition and computer vision for decades. One of the most representative and successful classification algorithms is the support vector machines (SVM) [ V apnik, 2013], which achieves an enormous success in pattern classification by minimizing the V apnik-Chervonenkis dimensions and structural risk. However, a standard SVM model is based on vector inputs and cannot directly deal with matrices or higher dimensional data structures, namely, tensors, which are very common in real-life applications.