The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.

Due to the lack of enough generalization in the state-space, common methods in Reinforcement Learning (RL) suffer from slow learning speed especially in the early learning trials. This paper introduces a model-based method in discrete state-spaces for increasing learning speed in terms of required experience (but not required computational time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation (full-space). Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the full-space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called Model Based Learning with Subspaces (MoBLeS), calculates confidence intervals of the estimated Q-values in the full-space and in the subspaces. These confidence intervals are used in the decision making, such that the agent benefits the most from the possible generalization while avoiding from detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to the optimal policy is theoretically investigated. Additionally, we show through several experiments that MoBLeS improves the learning speed in the early trials.

To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The external conditions are mapped into the cell through the activation of special proteins called transcription factors (TFs). Due to the difficult task to measure experimentally TF behaviours, and the challenges to capture their quick-time dynamics, different types of models based on differential equations have been proposed. However, those approaches usually incur in costly procedures, and they present problems to describe sudden changes in TF regulators. In this paper, we present a switched dynamical latent force model for reverse-engineering transcriptional regulation in gene expression data which allows the exact inference over latent TF activities driving some observed gene expressions through a linear differential equation. To deal with discontinuities in the dynamics, we introduce an approach that switches between different TF activities and different dynamical systems. This creates a versatile representation of transcription networks that can capture discrete changes and non-linearities We evaluate our model on both simulated data and real-data (e.g. microaerobic shift in E. coli, yeast respiration), concluding that our framework allows for the fitting of the expression data while being able to infer continuous-time TF profiles.

Conceptual spaces are geometric representations of conceptual knowledge, in which entities correspond to points, natural properties correspond to convex regions, and the dimensions of the space correspond to salient features. While conceptual spaces enable elegant models of various cognitive phenomena, the lack of automated methods for constructing such representations have so far limited their application in artificial intelligence. To address this issue, we propose a method which learns a vector-space embedding of entities from Wikipedia and constrains this embedding such that entities of the same semantic type are located in some lower-dimensional subspace. We experimentally demonstrate the usefulness of these subspaces as (approximate) conceptual space representations by showing, among others, that important features can be modelled as directions and that natural properties tend to correspond to convex regions.

People are rushing to trade in their iPhone 8 and iPhone 8 Plus devices before the iPhone X releases on Nov. 3. Decluttr, a site that buys electronics from consumers, said iPhone 8 trade-ins are "unusually high" as Apple gears up to launch the 10th anniversary device. Decluttr said it has seen more trade-ins of the 8 and 8 Plus received than any other previous new iPhone launched. The iPhone 8 was released last month and was revealed alongside the iPhone X. The iPhone X has more features than the iPhone 8 and iPhone 8 Plus, like the Face ID and Animoji.

DETROIT – Shares of General Motors hit an all-time Tuesday as investors focused on a $2.5 billion third-quarter pretax profit and ignored a big accounting loss. The Detroit automaker's $3 billion net loss came from a $5.4 billion charge for selling Opel and Vauxhall to France's PSA Group, which closed in August. But with that backed out and before taxes, the company made $1.32 per share, trouncing Wall Street estimates. Analysts polled by FactSet expected $1.11 per share. Much of the accounting charge came from previous losses that GM can't use to offset future tax obligations.

Mirowski cites Turing as author of the paragraph containing this remark. The paragraph appeared in [46], in a chapter with Turing listed as one of three contributors. Which parts of the chapter are the work of which contributor, particularly the introductory material containing this quote, is not made explicit.

Cambit pieces can be assembled to create a dozen different imaging systems. The cameras in our phones and tablets have turned us all into avid photographers, regularly using them to capture special moments and document our lives. One notable feature of camera phones is they are compact and fully automatic, enabling us to point and shoot without having to adjust any settings. However, when we need to capture photos of high aesthetic quality, we resort to more sophisticated DSLR cameras in which a variety of lenses and flashes can be used interchangeably. This flexibility is important for spanning the entire range of real-world imaging scenarios, while enabling us to be more creative. Many developers have sought to make these cameras even more flexible through both hardware and software.

While Samsung and Apple are busy preparing their Amazon Echo rivals, LG has launched a new Bluetooth speaker that banks on levitating technology to capture the attention of consumers. The LG PJ9 is a portable speaker that utilizes magnetic technology to keep it floating in the air for hours, and it is now available in the electronics company's home country. On Tuesday, LG officially launched the PJ9 Bluetooth speaker in South Korea, just two months after the product made its debut in the U.K. back in August. The company proudly shared that what makes its speaker noteworthy is the 360 degrees audio it provides thanks to its unique design and floating technology. LG says the PJ9 is capable of delivering good sound quality because the speaker itself is floating above the Woofer Station, as per Korea Herald. LG's Bluetooth speaker has two components: an egg-shaped speaker and a large docking base, which works as a subwoofer and charger.

A group of researchers from the UK and the US have used machine learning techniques to successfully predict earthquakes. Although their work was performed in a laboratory setting, the experiment closely mimics real-life conditions, and the results could be used to predict the timing of a real earthquake. The team, from the University of Cambridge, Los Alamos National Laboratory and Boston University, identified a hidden signal leading up to earthquakes, and used this'fingerprint' to train a machine learning algorithm to predict future earthquakes. Their results, which could also be applied to avalanches, landslides and more, are reported in the journal Geophysical Review Letters. For geoscientists, predicting the timing and magnitude of an earthquake is a fundamental goal.