Questions about computer algorithms that automatically discover patterns in data and make good decisions based on them.

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3
votes
1answer
31 views

Classification training data, but regression prediction

Suppose I'm performing machine learning on a simple dataset, and have a bunch of training data of the form: ...
0
votes
1answer
18 views

How to label a video for supervised learning?

I have just started discovering the machine learning world! I found many tutorials showing how to deal with different types of data. I walked through some tensorFlow tutorials and I think they have ...
2
votes
1answer
27 views

In TensorFlow tutorials, why do they use only the first term of cross-entropy as the cost function?

The cross-entropy cost function is usually defined as $$C = -\frac{1}{n} \sum_x \left[y \ln \hat{y} + (1-y ) \ln (1-\hat{y}) \right]$$ where $y$ is the expected output and $\hat{y}$ is the predicted ...
0
votes
0answers
10 views

Continuous Observation Densities in HMM

I've been reading about hidden Markov models and stumbled upon A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition by Lawrence R. Rabiner (Proc. IEEE, 77(2):257–...
0
votes
1answer
20 views

How to model “name similarity”?

I am new to machine learning. But have basic understanding of the concepts. Problem statement : We humans won't perceive much difference between "Tim Cook" and "Tim C0ok". ( 0 is been replaced with ...
1
vote
1answer
16 views

error computation in multi layered perceptron

I was reading about Multi Layered Perceptron(MLP) and how can we learn pattern using it. Algorithm was stated as Initiate all weight to small values. Compute activation of each neuron using sigmoid ...
0
votes
0answers
9 views

weaker condition than e-representative will suffice for agnostic PAC?

We were wondering, why is it necessary to demand that $|L_S(h)-L_D(h)|<\epsilon$ be true simultaneously for all $h\in H$. A much weaker demand would be that the inequality be true simultaneously ...
0
votes
0answers
4 views

Tensorflow understanding tf.train.shuffle_batch [migrated]

I have a single file of training data, about 100K rows, and I'm running a straightforward tf.train.GradientDescentOptimizer on each training step. The setup is ...
-4
votes
1answer
46 views

python, learn from users erros// multiple choice quiz [closed]

I am making a multiple choice quiz however I am finding difficulty on how to impletement a machine learning algrithm of somesort where by it can see which questions the user gets wrong and make a ...
1
vote
2answers
55 views

How to generate response variable during machine learning?

I am analyzing data of insurance companies and for some reasons the data that was provided doesn't have any response variable of whether an insurance claim is legit or suspicious. Are there any ways ...
0
votes
1answer
41 views

About the MNIST data-set

Is this known as a fact or from some analysis that the MNIST data-set is almost as if its sampled from some low (~10?) dimensional manifold? Is there a locally linear embedding to a low-dimensional ...
3
votes
1answer
19 views

Project to L1 ball of specified radius

The task If $x \in \mathbb{R}^d$ is a $d$-dimensional vector, recall that the $\ell_1$ norm of $x$ is given by $$||x||_1 = |x_1| + |x_2| + \dots + |x_d|.$$ The $\ell_1$-ball of radius $\lambda$ is ...
0
votes
0answers
10 views

How to build Life Time Value model?

Does anyone have any ideas about what would be a good way to go about building a user Life Time Value model? I have a website with two kinds of revenue, pay per click and impression from ...
-1
votes
0answers
36 views

SVD of a sum of two matrices when one is diagonal

I have an $n\times n$ rank-$k$ matrix $A$, and a diagonal $n\times n$ matrix $D$, and I have the svd of A, $$[U, S, V] = \mathrm{svd}(A)\,.$$ I want to compute the singular value decomposition (SVD) ...
-5
votes
0answers
18 views

How to solve the below case by machine learning?classifiers

I have to creat a classifier for below inputs and ouputs.
0
votes
2answers
45 views

Best starter resources for learning about AI [closed]

I want to start learning of AI and have an idea to program "social evolution simulator" but want to hear any advice to avoid creation "well known bicycles" and get strong knowledge-base. Will be ...
0
votes
1answer
22 views

Is TD-learning considered a model-based algorithm?

Differently from Sarsa and Q-learning, pure temporal difference learning (TD-learning) works with state value functions $V(s)$ and not state-action Q value functions $Q(s,a)$. It means that, in order ...
-3
votes
1answer
36 views

In what way can Google deepdream be extended? Is there an image dataset which can use hallucinations produced? Any deepdream useful application? [closed]

Is there a particular section of image data which when trained on deepdream algorithm and given some input image produce a resulting image from which we can conclude that the deepdream can be used for ...
0
votes
0answers
13 views

choosing right neural network architecture and input features

As a short example suppose we have a kind of pollution sensor and a jet fan in a tunnel. Jet fan turn on/off according to the automatic scenario based on pollution sensor value. Pollution itself ...
0
votes
0answers
41 views

Classifying responses into yes/no

So my problem is as follows: I get responses (such as "yeah whatever", "yes do it", "no don't do it", "nah", "yeah do it" etc.) and I need to classify them into either "yes" or "no" i.e. a binary ...
2
votes
1answer
24 views

Specific case of a ranking model

So the problem I'm solving is this: I have a list of conversations of 3 messages each (for eg. "hi", " how are you", "remind me to fix this bug" is one conversation, and my problem will have many of ...
4
votes
1answer
81 views

Is there a “flaw” in the backpropagation algorithm?

While trying to find a better backpropagation algorithm, I came across a paradox in my algorithm and then I found out this also happens in the usual backpropagation algorithm. Our neural network ...
2
votes
2answers
74 views

Are neural networks a type of reinforcement learning or are they different?

Can neural networks be considered a form of reinforcement learning or is there some essential difference between the two? By the same token could we consider neural networks a sub-class of genetic ...
2
votes
0answers
17 views

Is there any example of Regression Tree driven optimization (or active learning)?

Bayesian Optimization is the classic example of meta-model driven optimization where new observations are used to train a Gaussian process that provides a clue to where to optimize next. LEM (...
1
vote
0answers
31 views

Posterior of a DP in a Dirichlet process mixture model

Draws from a Dirichlet process (DP) are discrete, and exhibit clustering behaviour. Dirichlet Processes The Dirichlet Process ($DP$) is a stochastic process used in Bayesian nonparametric modelling....
3
votes
0answers
19 views

Inconsistent results with Q-learning

I'm running a q-learning agent in tic tac toe against a minimax agent and measuring the tie rate (since you can never win against minimax). When I run 10,000 training games, I've found that there seem ...
0
votes
0answers
10 views

What is the need of re-sampling the image for HOG features?

I read Dalal and Triggs paper for HOG description and a blog Chris McCormick HOG regarding the same. The blog says that the image needs to be re-sampled at different scales to recognize different ...
0
votes
0answers
14 views

tips on how to define a state space for reinforcement learning

I have read some reinforcement learning model examples of various things and I was surprised by how varied unintuitive some of the state spaces are. For example: in the standard grid world, it is ...
4
votes
1answer
63 views

Machine Learning: Identify Patterns in Time-Series Data

I work in renewable energy. My company gathers a lot of data from equipment. This typically includes process data (such as transformer temperature, line voltages, currents, etc.) and discrete alarms (...
0
votes
0answers
12 views

Does marginalizing on a Bayesian network preserve its original independence assumptions?

I know that marginalizing over a Bayesian network causes changes to the graph (e.g. marginalizing node c in the V-structure given by $a \rightarrow c \leftarrow b$ results in $a$ and $b$ being ...
2
votes
3answers
54 views

How to calculate IV, EV and optimal k for K-means?

Could someone explain how to calculate the following 3 evaluative properties: Intercluster Variability (IV) - How different are the data points within the same cluster Extracluster Variability (EV) -...
0
votes
0answers
20 views

How can I change the text style?

There are algorithms to change the forms of words, to define the subject of the text, as well as the definition of his style (literary, business, scientific, etc.). Do you have any ready ideas or ...
1
vote
0answers
23 views

Variable elimination in Bayesian network

I'm trying to check if my understanding of variable elimination is correct. Assume the above Bayesian network is factorized as: $p(a,b,d,e,l,s,t,x) = p(a)p(t|a)p(e|t,l)p(x|e)p(l|s)p(b|s)p(d|b,e)p(s)$...
0
votes
0answers
16 views

How to calculate total variability matrix?

I'm writing paper about speaker recognition using artificial neural networks and currently I'm stuck with one thing. There is a Gaussian Mixture Model (GMM) that we can use to represent speech and it ...
0
votes
0answers
54 views

How to design deep convolutional neural networks?

As I understand it, all CNNs are quite similar. They all have a convolutional layers followed by pooling and relu layers. Some have specialised layers like FlowNet and Segnet. My doubt is how should ...
0
votes
0answers
27 views

Supervised Machine Learning for Event Classification

I've got some general questions about supervised machine learning. I have many disparate systems, all of which generate event information. These could be things such as hardware failure, software ...
3
votes
0answers
50 views

Are coevolutionary “Free Lunches” really free lunches?

In their paper "Coevolutionary Free Lunches" David Wolpert and William Macready describe a set of exceptions to the No Free Lunch theorems they proved in an earlier paper. The exceptions involve two-...
1
vote
0answers
25 views

Inductive Bias - Decision Tree Pruning as a Bias

I am trying to understand inductive bias and have been looking around to try and work it out. I found this which explains what it is briefly but I have struggled to find examples I understand which ...
1
vote
1answer
24 views

Difference between SNN RL and DNN RL?

In Reinfrocement Learning (RL) in Neural Networks (NNs), I've seen two approaches to Q-learning. The first is to tile the state space with basis functions using Spiking Neural Networks (SNN) to ...
4
votes
0answers
26 views

Are the Confabulation Theories of Thaler and Hecht-Nielsen Isomorphic?

Both S. L. Thaler and R. Hecht-Nielsen have set forth neural-based theories of "confabulation" applicable to machine learning. The essential mathematics of Hecht-Nielsen is set forth in his paper "...
1
vote
1answer
34 views

reinforcement learning in gridworld with subgoals

Andrew Ng, Daishi Harada, Stuart Russell published a conference paper entitled Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. There is a specific example ...
2
votes
0answers
34 views

two ways of calculating the entropy in attribute selection (decision tree)

The definition of the entropy is $$H(Y) = -\sum p(y_j)\log_2 p(y_j)\,.$$ Now my text book says to compute the entropy for each attribute we consider the grouping of the data by that attribute now ...
2
votes
1answer
77 views

How do I stop “cheating” in reinforcement learning (MLP+Evo. Algorithm)?

I have a two hidden-layer MLP. I am trying to teach it classification of the sine function. For instance, if there is an [x,y] point above the sine function, the ANN should classify that point as a 1. ...
0
votes
1answer
21 views

What ML methods exist to categorize signal from noise? Red noise? Spatially correlated noise?

Let's say we are given measurements of some sort. In many cases, it is safe to assume that noise is white noise, serially uncorrelated, and zero mean with some finite variance. But in other cases, ...
1
vote
1answer
30 views

Find Correlations in Vectors of symbols

Given a set of vectors, lets say that each coordinate is populated from an alphabet (meaning set of symbols, numbers, etc) (particular or shared alphabets are indistinct). Is there any standard ...
2
votes
1answer
92 views

Why Isn't This Outlier Score/Reconstruction Error Not Squared?

I was looking through a paper called "AI2 : Training a big data machine to defend", and saw this (http://people.csail.mit.edu/kalyan/AI2_Paper.pdf) $score(X_{i}) = \sum_{j=1}^{p} (|X_{i} − R^{j}_{i}|)...
1
vote
0answers
16 views

How to mitigate the hierarchical error propagation in tree-structured classification

Suppose we have a multi-class classification problem, where the number of classes $K \geq 3$ We use a tree structure of multiple SVMs to divide and conquer the problem, with one example in the figure ...
2
votes
1answer
33 views

What is the Best and easiest way to create a Classifer for Sentiment Analysis [closed]

Sentiment analysis using Machine Learning is a hot topic. In the present situation when a person doesn't have a problem in having the training data set then which way should we create the classifier ...
0
votes
0answers
19 views

How do I choose the initial features vectors for a Stochastic Gradient Descent trained SVD++ algorithm?

I'm reading the SVD++ Netflix Recommender Systems paper because I want to be able to properly assess this approach to building a recommender system. How do I choose the initial values of $q_i$ and $...
1
vote
1answer
14 views

Using a combination of spatial and non-spatial inputs for convolutional neural networks

I'm working on training a game AI using deep reinforcement learning to achieve specific examples based on pixel input and some additional state information. Naturally, I'm using a convolutional ...