In defence of single-premise closure

It’s often thought that the phenomenon of risk aggregation poses a problem for multi-premise closure but not for single-premise closure. But recently, Lasonen-Aarnio and Schechter have challenged this thought. Lasonen-Aarnio argues that, insofar as risk aggregation poses a problem for multi-premise closure, it poses a similar problem for single-premise closure. For she thinks that, there being such a thing as deductive risk, risk may aggregate over a single premise and the deduction itself. Schechter argues that single-premise closure succumbs to risk aggregation outright. For he thinks that there could be a long sequence of competent single-premise deductions such that, even though we are justified in believing the initial premise of the sequence, intutively, we are not justified in believing the final conclusion. This intuition, Schechter thinks, vitiates single-premise closure. In this paper, I defend single-premise closure against the arguments offered by Lasonen-Aarnio and Schechter

Feature Selection For The Fuzzy Artmap Neural Network Using A Hybrid Genetic Algorithm And Tabu Search

Prestasi pengelas rangkaian neural amat bergantung kepada set data yang digunakan dalam process pembelajaran. The performance of Neural-Network (NN)-based classifiers is strongly dependent on the data set used for learning

Feature Selection For The Fuzzy Artmap Neural Network Using A Hybrid Genetic Algorithm And Tabu Search [QA76.87. T164 2007 f rb].

Prestasi pengelas rangkaian neural amat bergantung kepada set data yang digunakan dalam process pembelajaran. Secara praktik, set data berkemungkinan mengandungi maklumat yang tidak diperlukan. Dengan itu, pencarian ciri merupakan suatu langkah yang penting dalam pembinaan suatu pengelas berdasarkan rangkaian neural yang efektif. The performance of Neural-Network (NN)-based classifiers is strongly dependent on the data set used for learning. In practice, a data set may contain noisy or redundant data items. Thus, feature selection is an important step in building an effective and efficient NN-based classifier

Neural-network solutions to stochastic reaction networks

The stochastic reaction network is widely used to model stochastic processes in physics, chemistry and biology. However, the size of the state space increases exponentially with the number of species, making it challenging to investigate the time evolution of the chemical master equation for the reaction network. Here, we propose a machine-learning approach using the variational autoregressive network to solve the chemical master equation. The approach is based on the reinforcement learning framework and does not require any data simulated in prior by another method. Different from simulating single trajectories, the proposed approach tracks the time evolution of the joint probability distribution in the state space of species counts, and supports direct sampling on configurations and computing their normalized joint probabilities. We apply the approach to various systems in physics and biology, and demonstrate that it accurately generates the probability distribution over time in the genetic toggle switch, the early life self-replicator, the epidemic model and the intracellular signaling cascade. The variational autoregressive network exhibits a plasticity in representing the multi-modal distribution by feedback regulations, cooperates with the conservation law, enables time-dependent reaction rates, and is efficient for high-dimensional reaction networks with allowing a flexible upper count limit. The results suggest a general approach to investigate stochastic reaction networks based on modern machine learning

Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing

Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of $impression \rightarrow click \rightarrow conversion$ is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connected with an explicitly defined function or implicitly transferred information in current works. These methods alleviate the data sparsity problem for long-path sequential tasks as the positive feedback becomes sparser along with the task sequence. However, the error accumulation and negative transfer will be a severe problem for downstream tasks. Especially, at the beginning stage of training, the optimization for parameters of former tasks is not converged yet, and thus the information transferred to downstream tasks is negative. In this paper, we propose a prior information merged model (\textbf{PIMM}), which explicitly models the logical dependence among tasks with a novel prior information merged (\textbf{PIM}) module for multiple sequential dependence task learning in a curriculum manner. Specifically, the PIM randomly selects the true label information or the prior task prediction with a soft sampling strategy to transfer to the downstream task during the training. Following an easy-to-difficult curriculum paradigm, we dynamically adjust the sampling probability to ensure that the downstream task will get the effective information along with the training. The offline experimental results on both public and product datasets verify that PIMM outperforms state-of-the-art baselines. Moreover, we deploy the PIMM in a large-scale FinTech platform, and the online experiments also demonstrate the effectiveness of PIMM

Point-of-Interest Recommendation Algorithm Based on User Similarity in Location-Based Social Networks

Location-based social network is rising recent years with the development of mobile internet, and point-of-interest (POI) recommendation is a hot topic of this field. Because the factors that affect the behavior of users are very complex, most of the research focuses on the context of the recommendation. But overall context data acquisition in practice is often difficult to obtain. In this paper, we have considered the most common collaborative recommendation algorithm based on user similarity, and discussed several methods of user similarity definition. Comparing the effect of different methods in the actual dataset, experimental results show among the factors including that social relation, check-in and geographical location the check-in is extremely important, so this work is of certain guiding significance to the actual applications

Transparency and Partial Beliefs

How should we account for self-knowledge of our inner lives? Some have argued that just as we have various senses that allow us to perceive the environment, we have an inner sense that allows us to perceive our inner lives. But others find such a view implausible and think that there are other ways to account for self-knowledge. With respect to all-or-nothing beliefs, some have held that we may account for self-knowledge by appealing to the claim that such beliefs are transparent--that we may answer the question 'Do you believe p?' by answering the question 'Is it the case that p?' But surprisingly, little or no attention has been paid to the question of whether partial beliefs are transparent. In this paper, I clarify the question of whether partial beliefs are transparent. I also consider various attempts to answer the question in the affirmative. To anticipate, my verdict is pessimistic: I argue that such attempts fail