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There have been success modeling card games with reinforcement learning (RL) using Deep Q-learning Network (DQN) with experience replay. Experience replay buffer is a large collection of tuples: (state (s), action (a), reward (r), next state (s′). An RL agent can learn which combinations lead to the highest reward. The representations are stored in a deep ...


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Better approach would definitely be supervised learning model. There are two alternatives for you to go: (1) What you could try is to use a transformer model that was trained on another sentiment case, like movie or restaurant reviews. First, you could try how this model works for your use-case and then use it to label your unlabeled data. (2) Or you could ...


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This problem requires you to distinguish customer behavior . One customer’s purchase pattern may be different from another. I am assuming that you are collecting this data daily . Let’s assume that the data for each day is a vector In multi- dimensional space . you can calculate Mahalanobis distance between the vector for current day which will be of ...


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One option is hierarchical clustering which builds a hierarchy of clusters.


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You are finding about Semi-supervised object detection algorithm and Weakly-supervised object detection. Semi-supervised object detection uses Supervised-learning term (Your handmade labeled data) and Semi-supervised learning term (Unlabeled data). Weakly-supervised object detection uses coarse-grained data which is imperfect, inaccurate, or partial. For ...


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Eventually, since the data includes only phone calls, I have noticed that there is a "BIP" that separates the conversation from the music at the beginning. So I convolved it over files and achieved better results than k-means and GMMs.


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You can not apply classification since you do not have ground-truth labels for the features. Labeling features might be more work than just directly cleaning the features. There are data wrangling tools like Trifacta and Datameer that are designed for this type of problem.


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In my opinion there are two main problems with your approach: The clustering is extremely unlikely to correspond to sentiment, unless the features that you use for clustering are specifically engineered to represent sentiment. In general text clustering tend to group documents by common words, i.e. similar topic. This might lead to different categories of ...


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